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Brunschwiler F, Nakka S, Guerra J, Guarda G. A Ménage à trois: NLRC5, immunity, and metabolism. Front Immunol 2024; 15:1426620. [PMID: 39035010 PMCID: PMC11257985 DOI: 10.3389/fimmu.2024.1426620] [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: 05/01/2024] [Accepted: 06/19/2024] [Indexed: 07/23/2024] Open
Abstract
The nucleotide-binding and oligomerization domain-like receptors (NLRs) NLR family CARD domain-containing protein 5 (NLRC5) and Class II Major Histocompatibility Complex Transactivator (CIITA) are transcriptional regulators of major histocompatibility complex (MHC) class I and class II genes, respectively. MHC molecules are central players in our immune system, allowing the detection of hazardous 'non-self' antigens and, thus, the recognition and elimination of infected or transformed cells from the organism. Recently, CIITA and NLRC5 have emerged as regulators of selected genes of the butyrophilin (BTN) family that interestingly are located in the extended MHC locus. BTNs are transmembrane proteins exhibiting structural similarities to B7 family co-modulatory molecules. The family member BTN2A2, which indeed contributes to the control of T cell activation, was found to be transcriptionally regulated by CIITA. NLRC5 emerged instead as an important regulator of the BTN3A1, BTN3A2, and BTN3A3 genes. Together with BTN2A1, BTN3As regulate non-conventional Vγ9Vδ2 T cell responses triggered by selected metabolites of microbial origin or accumulating in hematologic cancer cells. Even if endogenous metabolites conform to the canonical definition of 'self', metabolically abnormal cells can represent a danger for the organism and should be recognized and controlled by immune system cells. Collectively, new data on the role of NLRC5 in the expression of BTN3As link the mechanisms regulating canonical 'non-self' presentation and those marking cells with abnormal metabolic configurations for immune recognition, an evolutionary parallel that we discuss in this perspective review.
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Affiliation(s)
| | | | - Jessica Guerra
- Università della Svizzera Italiana (USI), Faculty of Biomedical Sciences, Institute for Research in Biomedicine, Bellinzona, Switzerland
| | - Greta Guarda
- Università della Svizzera Italiana (USI), Faculty of Biomedical Sciences, Institute for Research in Biomedicine, Bellinzona, Switzerland
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Bauer S, Hezinger L, Rexhepi F, Ramanathan S, Kufer TA. NOD-like Receptors-Emerging Links to Obesity and Associated Morbidities. Int J Mol Sci 2023; 24:ijms24108595. [PMID: 37239938 DOI: 10.3390/ijms24108595] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/06/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Obesity and its associated metabolic morbidities have been and still are on the rise, posing a major challenge to health care systems worldwide. It has become evident over the last decades that a low-grade inflammatory response, primarily proceeding from the adipose tissue (AT), essentially contributes to adiposity-associated comorbidities, most prominently insulin resistance (IR), atherosclerosis and liver diseases. In mouse models, the release of pro-inflammatory cytokines such as TNF-alpha (TNF-α) and interleukin (IL)-1β and the imprinting of immune cells to a pro-inflammatory phenotype in AT play an important role. However, the underlying genetic and molecular determinants are not yet understood in detail. Recent evidence demonstrates that nucleotide-binding and oligomerization domain (NOD)-like receptor (NLR) family proteins, a group of cytosolic pattern recognition receptors (PRR), contribute to the development and control of obesity and obesity-associated inflammatory responses. In this article, we review the current state of research on the role of NLR proteins in obesity and discuss the possible mechanisms leading to and the outcomes of NLR activation in the obesity-associated morbidities IR, type 2 diabetes mellitus (T2DM), atherosclerosis and non-alcoholic fatty liver disease (NAFLD) and discuss emerging ideas about possibilities for NLR-based therapeutic interventions of metabolic diseases.
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Affiliation(s)
- Sarah Bauer
- Institute of Nutritional Medicine, Department of Immunology, University of Hohenheim, 70593 Stuttgart, Germany
| | - Lucy Hezinger
- Institute of Nutritional Medicine, Department of Immunology, University of Hohenheim, 70593 Stuttgart, Germany
| | - Fjolla Rexhepi
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Sheela Ramanathan
- Department of Immunology and Cell Biology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Thomas A Kufer
- Institute of Nutritional Medicine, Department of Immunology, University of Hohenheim, 70593 Stuttgart, Germany
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Duan M, Zhang L, Wang Y, Fan Y, Liu S, Yu Q, Huang L, Zhou F. Computational pan-cancer characterization of model-based quantitative transcription regulations dysregulated in regional lymph node metastasis. Comput Biol Med 2021; 135:104571. [PMID: 34166881 DOI: 10.1016/j.compbiomed.2021.104571] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
Cancer is one of the major causes of mortality worldwide. Regional lymph node metastasis is an important mechanism during the spread of human cancers, in which transcription regulation plays an essential role. This study formulated a regression-model-based quantitative transcription regulation (mqTrans) between one mRNA gene and multiple transcription factors (TFs). Computational pan-cancer screening was carried out to detect the quantitative dysregulation of transcription regulation in the regional lymph node metastasis of 18 cancer types. Only a few metastasis-dysregulated mqTrans models were shared among the cancer types. The mRNA genes of the metastasis-dysregulated mqTrans models were not differentially expressed in regional lymph node metastasis. The experimental data suggested that mqTrans technology provided a complementary approach to the evaluation of transcription regulation mechanisms and may facilitate its quantitative investigation in other phenotypes.
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Affiliation(s)
- Meiyu Duan
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Lei Zhang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Yueying Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin Province, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Yusi Fan
- College of Software, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Shuai Liu
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin Province, China
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.
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