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Karagiannis TT, Dowrey TW, Villacorta-Martin C, Montano M, Reed E, Belkina AC, Andersen SL, Perls TT, Monti S, Murphy GJ, Sebastiani P. Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity. EBioMedicine 2023; 90:104514. [PMID: 37005201 PMCID: PMC10114155 DOI: 10.1016/j.ebiom.2023.104514] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 01/27/2023] [Accepted: 02/22/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Age-related changes in immune cell composition and functionality are associated with multimorbidity and mortality. However, many centenarians delay the onset of aging-related disease suggesting the presence of elite immunity that remains highly functional at extreme old age. METHODS To identify immune-specific patterns of aging and extreme human longevity, we analyzed novel single cell profiles from the peripheral blood mononuclear cells (PBMCs) of a random sample of 7 centenarians (mean age 106) and publicly available single cell RNA-sequencing (scRNA-seq) datasets that included an additional 7 centenarians as well as 52 people at younger ages (20-89 years). FINDINGS The analysis confirmed known shifts in the ratio of lymphocytes to myeloid cells, and noncytotoxic to cytotoxic cell distributions with aging, but also identified significant shifts from CD4+ T cell to B cell populations in centenarians suggesting a history of exposure to natural and environmental immunogens. We validated several of these findings using flow cytometry analysis of the same samples. Our transcriptional analysis identified cell type signatures specific to exceptional longevity that included genes with age-related changes (e.g., increased expression of STK17A, a gene known to be involved in DNA damage response) as well as genes expressed uniquely in centenarians' PBMCs (e.g., S100A4, part of the S100 protein family studied in age-related disease and connected to longevity and metabolic regulation). INTERPRETATION Collectively, these data suggest that centenarians harbor unique, highly functional immune systems that have successfully adapted to a history of insults allowing for the achievement of exceptional longevity. FUNDING TK, SM, PS, GM, SA, TP are supported by NIH-NIAUH2AG064704 and U19AG023122. MM and PS are supported by NIHNIA Pepper center: P30 AG031679-10. This project is supported by the Flow Cytometry Core Facility at BUSM. FCCF is funded by the NIH Instrumentation grant: S10 OD021587.
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Affiliation(s)
- Tanya T Karagiannis
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
| | - Todd W Dowrey
- Center for Regenerative Medicine (CReM), Boston University and Boston Medical Center, Boston, MA, USA
| | - Carlos Villacorta-Martin
- Center for Regenerative Medicine (CReM), Boston University and Boston Medical Center, Boston, MA, USA
| | - Monty Montano
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Boston Pepper Center, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Eric Reed
- Data Intensive Study Center, Tufts University, Boston, MA, USA
| | - Anna C Belkina
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA; Flow Cytometry Core Facility, Boston University School of Medicine, Boston, MA, USA
| | - Stacy L Andersen
- Department of Medicine, Geriatrics Section, Boston University School of Medicine, Boston, MA, USA
| | - Thomas T Perls
- Department of Medicine, Geriatrics Section, Boston University School of Medicine, Boston, MA, USA
| | - Stefano Monti
- Bioinformatics Program, Boston University, Boston, MA, USA; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - George J Murphy
- Center for Regenerative Medicine (CReM), Boston University and Boston Medical Center, Boston, MA, USA; Section of Hematology and Medical Oncology, Boston University School of Medicine, Boston, MA, USA
| | - Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA; Department of Medicine, Tufts University, Boston, MA, USA
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Kim S, Reed E, Monti S, Schlezinger JJ. A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens. Environ Health Perspect 2021; 129:77006. [PMID: 34323617 PMCID: PMC8320370 DOI: 10.1289/ehp6886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
BACKGROUND Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor-γ (PPARγ) to generate adipocytes with distinct phenotypes. OBJECTIVES Our objectives were to a) establish a novel classification method to predict PPARγ ligands and modifying chemicals; and b) create a taxonomy to group chemicals on the basis of their effects on PPARγ's transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic PPARγ ligands, would induce white but not brite adipogenesis. METHODS 3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, nuclear receptor ligands known to influence adipocyte biology, potential environmental PPARγ ligands). Differentiation was assessed by measuring lipid accumulation. mRNA expression was determined by RNA-sequencing (RNA-Seq) and validated by reverse transcription-quantitative polymerase chain reaction. A novel classification model was developed using an amended random forest procedure. A subset of environmental contaminants identified as strong PPARγ agonists were analyzed by their effects on lipid handling, mitochondrial biogenesis, and cellular respiration in 3T3-L1 cells and human preadipocytes. RESULTS We used lipid accumulation and RNA-Seq data to develop a classification system that a) identified PPARγ agonists; and b) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong PPARγ activation. 3T3-L1 cells treated with two known environmental PPARγ ligands, tetrabromobisphenol A and triphenyl phosphate, which sorted distinctly from therapeutic ligands, had higher expression of white adipocyte genes but no difference in Pgc1a and Ucp1 expression, and higher fatty acid uptake but not mitochondrial biogenesis. Moreover, cells treated with two chemicals identified as highly ranked PPARγ agonists, tonalide and quinoxyfen, induced white adipogenesis without the concomitant health-promoting characteristics of brite adipocytes in mouse and human preadipocytes. DISCUSSION A novel classification procedure accurately identified environmental chemicals as PPARγ ligands distinct from known PPARγ-activating therapeutics. CONCLUSION The computational and experimental framework has general applicability to the classification of as-yet uncharacterized chemicals. https://doi.org/10.1289/EHP6886.
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Affiliation(s)
- Stephanie Kim
- Boston University Superfund Research Program, Boston University, Massachusetts, USA
- Department of Environmental Health, Boston University School of Public Health, Massachusetts, USA
| | - Eric Reed
- Boston University Superfund Research Program, Boston University, Massachusetts, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Massachusetts, USA
- Boston University Bioinformatics Program, Boston University, Massachusetts, USA
| | - Stefano Monti
- Boston University Superfund Research Program, Boston University, Massachusetts, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Massachusetts, USA
- Boston University Bioinformatics Program, Boston University, Massachusetts, USA
| | - Jennifer J. Schlezinger
- Boston University Superfund Research Program, Boston University, Massachusetts, USA
- Department of Environmental Health, Boston University School of Public Health, Massachusetts, USA
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