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He X, Wang X, van Heck J, van Cranenbroek B, van Rijssen E, Stienstra R, Netea MG, Joosten I, Tack CJ, Koenen HJPM. Blood immune cell profiling in adults with longstanding type 1 diabetes is associated with macrovascular complications. Front Immunol 2024; 15:1401542. [PMID: 39011037 PMCID: PMC11246869 DOI: 10.3389/fimmu.2024.1401542] [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: 03/15/2024] [Accepted: 06/13/2024] [Indexed: 07/17/2024] Open
Abstract
Aims/hypothesis There is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity. Methods Adult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects. Results Hierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications. Conclusions Machine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.
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Affiliation(s)
- Xuehui He
- Department of Laboratory Medicine - Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Xinhui Wang
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Julia van Heck
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bram van Cranenbroek
- Department of Laboratory Medicine - Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Esther van Rijssen
- Department of Laboratory Medicine - Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Rinke Stienstra
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
- Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Irma Joosten
- Department of Laboratory Medicine - Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Cees J. Tack
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, Netherlands
| | - Hans J. P. M. Koenen
- Department of Laboratory Medicine - Medical Immunology, Radboud University Medical Center, Nijmegen, Netherlands
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2
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Joshi G, Das A, Verma G, Guchhait P. Viral infection and host immune response in diabetes. IUBMB Life 2024; 76:242-266. [PMID: 38063433 DOI: 10.1002/iub.2794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 11/05/2023] [Indexed: 04/24/2024]
Abstract
Diabetes, a chronic metabolic disorder disrupting blood sugar regulation, has emerged as a prominent silent pandemic. Uncontrolled diabetes predisposes an individual to develop fatal complications like cardiovascular disorders, kidney damage, and neuropathies and aggravates the severity of treatable infections. Escalating cases of Type 1 and Type 2 diabetes correlate with a global upswing in diabetes-linked mortality. As a growing global concern with limited preventive interventions, diabetes necessitates extensive research to mitigate its healthcare burden and assist ailing patients. An altered immune system exacerbated by chronic hyperinflammation heightens the susceptibility of diabetic individuals to microbial infections, including notable viruses like SARS-CoV-2, dengue, and influenza. Given such a scenario, we scrutinized the literature and compiled molecular pathways and signaling cascades related to immune compartments in diabetics that escalate the severity associated with the above-mentioned viral infections in them as compared to healthy individuals. The pathogenesis of these viral infections that trigger diabetes compromises both innate and adaptive immune functions and pre-existing diabetes also leads to heightened disease severity. Lastly, this review succinctly outlines available treatments for diabetics, which may hold promise as preventive or supportive measures to effectively combat these viral infections in the former.
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Affiliation(s)
- Garima Joshi
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India
| | - Anushka Das
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India
| | - Garima Verma
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India
| | - Prasenjit Guchhait
- Regional Centre for Biotechnology, National Capital Region Biotech Science Cluster, Faridabad, India
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Rostoka E, Shvirksts K, Salna E, Trapina I, Fedulovs A, Grube M, Sokolovska J. Prediction of type 1 diabetes with machine learning algorithms based on FTIR spectral data in peripheral blood mononuclear cells. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:4926-4937. [PMID: 37721124 DOI: 10.1039/d3ay01080e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
The incidence of autoimmunity is increasing, to ensure timely and comprehensive treatment, there must be a diagnostic method or markers that would be available to the general public. Fourier-transform infrared spectroscopy (FTIR) is a relatively inexpensive and accurate method for determining metabolic fingerprint. The metabolism, molecular composition and function of blood cells vary according to individual physiological and pathological conditions. Thus, by obtaining autoimmune disease-specific metabolic fingerprint markers in peripheral blood mononuclear cells (PBMC) and subsequently using machine learning algorithms, it might be possible to create a tool that will allow the diagnosis of autoimmune diseases. In this preliminary study, it was found that the peak shift at 1545 cm-1 could be considered specific for autoimmune disease type 1 diabetes (T1D), while the shifts at 1070 and 1417 cm-1 could be more attributed to the autoimmune condition per se. The prediction of T1D, despite the small number of participants in the study, showed an inverse AUC = 0.33 ± 0.096, n = 15, indicating a stable trend in the prediction of T1D based on FTIR metabolic fingerprint data in the PBMC.
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Affiliation(s)
- Evita Rostoka
- Faculty of Medicine, University of Latvia, Jelgavas iela 3, LV 1004, Riga, Latvia.
| | - Karlis Shvirksts
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas iela 1, LV1004, Riga, Latvia
| | - Edgars Salna
- Faculty of Medicine, University of Latvia, Jelgavas iela 3, LV 1004, Riga, Latvia.
| | - Ilva Trapina
- Institute of Biology, University of Latvia, Jelgavas iela 1, LV1004 Riga, Latvia
| | - Aleksejs Fedulovs
- Faculty of Medicine, University of Latvia, Jelgavas iela 3, LV 1004, Riga, Latvia.
| | - Mara Grube
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas iela 1, LV1004, Riga, Latvia
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Shapiro MR, Dong X, Perry DJ, McNichols JM, Thirawatananond P, Posgai AL, Peters LD, Motwani K, Musca RS, Muir A, Concannon P, Jacobsen LM, Mathews CE, Wasserfall CH, Haller MJ, Schatz DA, Atkinson MA, Brusko MA, Bacher R, Brusko TM. Human immune phenotyping reveals accelerated aging in type 1 diabetes. JCI Insight 2023; 8:e170767. [PMID: 37498686 PMCID: PMC10544250 DOI: 10.1172/jci.insight.170767] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023] Open
Abstract
The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.
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Affiliation(s)
- Melanie R. Shapiro
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Xiaoru Dong
- Diabetes Institute and
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Daniel J. Perry
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - James M. McNichols
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Puchong Thirawatananond
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Amanda L. Posgai
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Leeana D. Peters
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Keshav Motwani
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Richard S. Musca
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Andrew Muir
- Department of Pediatrics, Emory University, Atlanta, Georgia, USA
| | - Patrick Concannon
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Genetics Institute and
| | - Laura M. Jacobsen
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Clayton E. Mathews
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Clive H. Wasserfall
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Michael J. Haller
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Desmond A. Schatz
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Mark A. Atkinson
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Maigan A. Brusko
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
| | - Rhonda Bacher
- Diabetes Institute and
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, Florida, USA
| | - Todd M. Brusko
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, and
- Diabetes Institute and
- Department of Pediatrics, College of Medicine, University of Florida, Gainesville, Florida, USA
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Larsson H, Albinsson Högberg S, Lind M, Rabe H, Lingblom C. Investigating immune profile by CyTOF in individuals with long-standing type 1 diabetes. Sci Rep 2023; 13:8171. [PMID: 37210405 DOI: 10.1038/s41598-023-35300-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/16/2023] [Indexed: 05/22/2023] Open
Abstract
Type 1 diabetes (T1D) is an autoimmune disease caused by T-cell mediated destruction of pancreatic beta cells. Eosinophils are found in pancreatic tissue from individuals with T1D. Eosinophilic suppression of T cells is dependent of the protein galectin-10. Little is known when it comes to the role of eosinophil granulocytes in type 1 diabetes. Here we show that individuals with long-standing T1D had lower levels of galectin-10hi eosinophils and a subgroup of galectin-10hi eosinophils were entirely absent in all T1D patients. In addition, 7% immature eosinophils were present in the circulation of T1D patients whereas 0.8% in healthy individuals. Furthermore, higher levels of CD4+CD8+ T cells and Th17 cells were observed in patients with T1D. Blood samples from 12 adult individuals with long-standing T1D and 12 healthy individuals were compared using cytometry by time-of-flight. Lower levels of galectin-10hi eosinophils, which are potent T cell suppressors, in individuals with T1D could indicate that activated T cells are enabled to unrestrictedly kill the insulin producing beta cells. This is the first study showing absence of galectin-10hi eosinophilic subgroup in individuals with T1D compared with healthy controls. This study is a first important step toward unraveling the role of the eosinophils in patients with T1D.
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Affiliation(s)
- Helen Larsson
- Department of ENT, Head and Neck Surgery, NU Hospital Group, Trollhättan, Sweden
- Department of Otorhinolaryngology, Head and Neck Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
| | - Sofie Albinsson Högberg
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10A, 41346, Göteborg, Sweden
| | - Marcus Lind
- Department of Medicine, NU Hospital Group, Uddevalla, Trollhättan, Sweden
- Department of Molecular and Clinical Medicine, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden
- Department of Medicine, Sahlgrenska University Hospital, Göteborg, Region Västra Götaland, Sweden
| | - Hardis Rabe
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10A, 41346, Göteborg, Sweden
- RISE Research Institutes of Sweden, Bioscience and Materials, Göteborg, Sweden
| | - Christine Lingblom
- Department of Infectious Diseases, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Guldhedsgatan 10A, 41346, Göteborg, Sweden.
- Department of Clinical Microbiology, Sahlgrenska University Hospital, Göteborg, Region Västra Götaland, Sweden.
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Milosevic E, Babic A, Iovino L, Markovic M, Grce M, Greinix H. Use of the NIH consensus criteria in cellular and soluble biomarker research in chronic graft-versus-host disease: A systematic review. Front Immunol 2022; 13:1033263. [DOI: 10.3389/fimmu.2022.1033263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesChronic graft-versus-host disease (cGvHD) is the most frequent cause of late non-relapse mortality after allogeneic haematopoietic stem cell transplantation (alloHCT). Nevertheless, established biomarkers of cGvHD are still missing. The National Institutes of Health (NIH) Consensus Development Project on Criteria for Clinical Trials in cGvHD provided recommendations for biomarker research. We evaluated to which extent studies on cellular and soluble biomarkers in cGvHD published in the last 10 years complied with these recommendations. Also, we highlight the most promising biomarker candidates, verified in independent cohorts and/or repeatedly identified by separate studies.MethodsWe searched Medline and EMBASE for “cGvHD”, “biomarkers”, “soluble” and “cells” as MeSH terms or emtree subject headings, and their variations on July 28th, 2021, limited to human subjects, English language and last ten years. Reviews, case reports, conference abstracts and single nucleotide polymorphism studies were excluded. Criteria based on the set of recommendations from the NIH group for biomarker research in cGvHD were used for scoring and ranking the references.ResultsA total of 91 references encompassing 15,089 participants were included, 54 prospective, 17 retrospective, 18 cross-sectional, and 2 studies included both prospective and retrospective cohorts. Thirty-five papers included time-matched controls without cGvHD and 20 studies did not have any control subjects. Only 9 studies were randomized, and 8 were multicentric. Test and verification cohorts were included in 11 studies. Predominantly, diagnostic biomarkers were explored (n=54). Assigned scores ranged from 5-34. None of the studies fulfilled all 24 criteria (48 points). Nevertheless, the scores improved during the last years. Three cell subsets (CXCR3+CD56bright NK cells, CD19+CD21low and BAFF/CD19+ B cells) and several soluble factors (BAFF, IL-15, CD163, DKK3, CXCL10 and the panel of ST2, CXCL9, MMP3 and OPN) had the highest potential as diagnostic and/or prognostic biomarkers in cGvHD.ConclusionDespite several limitations of this review (limited applicability for paediatric population, definition of verification, missing data on comorbidities), we identified promising candidate biomarkers for further evaluation in multicentre collaborative studies. This review confirms the importance of the NIH consensus group criteria for improving the quality and reproducibility of cGvHD biomarker research.
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Tang R, Zhong T, Fan L, Xie Y, Li J, Li X. Enhanced T Cell Glucose Uptake Is Associated With Progression of Beta-Cell Function in Type 1 Diabetes. Front Immunol 2022; 13:897047. [PMID: 35677051 PMCID: PMC9168918 DOI: 10.3389/fimmu.2022.897047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Background Abnormal intracellular glucose/fatty acid metabolism of T cells has tremendous effects on their immuno-modulatory function, which is related to the pathogenesis of autoimmune diseases. However, the association between the status of intracellular metabolism of T cells and type 1 diabetes is unclear. This study aimed to investigate the uptake of glucose and fatty acids in T cells and its relationship with disease progression in type 1 diabetes. Methods A total of 86 individuals with type 1 diabetes were recruited to detect the uptake of glucose and fatty acids in T cells. 2-NBDG uptake and expression of glucose transporter 1 (GLUT1); or BODIPY uptake and expression of carnitine palmitoyltransferase 1A(CPT1A) were used to assess the status of glucose or fatty acid uptake in T cells. Patients with type 1 diabetes were followed up every 3-6 months for 36 months, the progression of beta-cell function was assessed using generalized estimating equations, and survival analysis was performed to determine the status of beta-cell function preservation (defined as 2-hour postprandial C-peptide >200 pmol/L). Results Patients with type 1 diabetes demonstrated enhanced intracellular glucose uptake of T cells as indicated by higher 2NBDG uptake and GLUT1 expression, while no significant differences in fatty acid uptake were observed. The increased T cells glucose uptake is associated with lower C-peptide and higher hemoglobin A1c levels. Notably, patients with low T cell glucose uptake at onset maintained high levels of C-peptide within 36 months of the disease course [fasting C-petite and 2-hour postprandial C-peptide are 60.6 (95%CI: 21.1-99.8) pmol/L and 146.3 (95%CI: 14.1-278.5) pmol/L higher respectively], And they also have a higher proportion of beta-cell function preservation during this follow-up period (P<0.001). Conclusions Intracellular glucose uptake of T cells is abnormally enhanced in type 1 diabetes and is associated with beta-cell function and its progression.
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Affiliation(s)
- Rong Tang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ting Zhong
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Li Fan
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuting Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Juan Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China
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8
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Sahin Tekin M, Kocaturk E, Gurcu S, Kayadibi H, Dibeklioglu B, Yorulmaz G. Cellular Immunity In Subacute Thyroiditis: A New Perspective Through Neopterin. Clin Exp Immunol 2022; 209:109-114. [PMID: 35576515 PMCID: PMC9307230 DOI: 10.1093/cei/uxac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 04/27/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Subacute thyroiditis (SAT) is an inflammatory disorder of the thyroid gland. Although its etiology is not fully understood, it is believed to occur shortly after viral infections and is mostly associated with human leukocyte antigen (HLA)-B*35. Cellular immunity is prominent in SAT. Neopterin is produced by activated monocytes/macrophages and is a marker of cellular immunity. Its production is stimulated by interferon gamma (IFN-γ), provided mainly by activated helper T lymphocytes type 1 (Th1) in the adaptive immune system. Therefore, with these cells' activation, an increase in serum neopterin levels is expected. We aimed to evaluate neopterin levels in demonstrating cellular immunity in SAT and compared 15 SAT patients with 16 healthy controls. Since all SAT patients were in the active thyrotoxic phase, we found a significant difference in thyroid functions, classical inflammatory markers, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP), were markedly elevated in the patient group. Although we expected to find an increase considering that cellular immunity is at the forefront in the pathogenesis of SAT, we found serum neopterin levels significantly lower in the patient group than in the control group. There is an increase in CD8+ T cells in the thyroid tissue in SAT. The possible relationship with HLA-B*35- MHC class I in SAT, and the antigen presentation to CD8+ T cells may be the reason why we observed low serum neopterin levels in patients due to the cytokine imbalance. Neopterin provides unique and independent data from classical acute phase response indicators.
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Affiliation(s)
- Melisa Sahin Tekin
- Department of Internal Medicine, Eskisehir Osmangazi University, Faculty of Medicine, Eskisehir, Turkey
| | - Evin Kocaturk
- Department of Biochemistry, Eskisehir Osmangazi University, Faculty of Medicine, Eskisehir, Turkey
| | - Sinem Gurcu
- Department of Pharmacy, Eskisehir City Hospital, Eskisehir, Turkey
| | - Huseyin Kayadibi
- Department of Biochemistry, Eskisehir Osmangazi University, Faculty of Medicine, Eskisehir, Turkey
| | - Bilge Dibeklioglu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Eskisehir Osmangazi University, Faculty of Medicine, Eskisehir, Turkey
| | - Goknur Yorulmaz
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Eskisehir Osmangazi University, Faculty of Medicine, Eskisehir, Turkey
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9
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Tang Y, Wang J, Zhang Y, Li J, Chen M, Gao Y, Dai M, Lin S, He X, Wu C, Shi X. Single-Cell RNA Sequencing Identifies Intra-Graft Population Heterogeneity in Acute Heart Allograft Rejection in Mouse. Front Immunol 2022; 13:832573. [PMID: 35222420 PMCID: PMC8866760 DOI: 10.3389/fimmu.2022.832573] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Transplant rejection remains a major barrier to graft survival and involves a diversity of cell types. However, the heterogeneity of each cell type in the allograft remains poorly defined. In the present study, we used single-cell RNA sequencing technology to analyze graft-infiltrating cells to describe cell types and states associated with acute rejection in a mouse heart transplant model. Unsupervised clustering analysis revealed 21 distinct cell populations. Macrophages formed five cell clusters: two resident macrophage groups, two infiltrating macrophage groups and one dendritic cell-like monocyte group. Infiltrating macrophages were predominantly from allogeneic grafts. Nevertheless, only one infiltrating macrophage cluster was in an active state with the upregulation of CD40, Fam26f and Pira2, while the other was metabolically silent. Re-clustering of endothelial cells identified five subclusters. Interestingly, one of the endothelial cell populations was almost exclusively from allogeneic grafts. Further analysis of this population showed activation of antigen processing and presentation pathway and upregulation of MHC class II molecules. In addition, Ubiquitin D was specifically expressed in such endothelial cell population. The upregulation of Ubiquitin D in rejection was validated by staining of mouse heart grafts and human kidney biopsy specimens. Our findings present a comprehensive analysis of intra-graft cell heterogeneity, describe specific macrophage and endothelial cell populations which mediate rejection, and provide a potential predictive biomarker for rejection in the clinic.
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Affiliation(s)
- Yunhua Tang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Jiali Wang
- Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yixi Zhang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Jun Li
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Maogen Chen
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Yifang Gao
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Meiqin Dai
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Shengjie Lin
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Xiaoshun He
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Chenglin Wu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
| | - Xiaomin Shi
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China.,Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China
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