{"doi":"10.1111/acel.14461","title":"Age‐Dependent Bi‐Phasic Dynamics of Ly49<sup>+</sup><scp>CD8</scp><sup>+</sup> Regulatory T Cell Population","abstract":"ABSTRACT Aging is tightly associated with reduced immune protection but increased risk of autoimmunity and inflammatory conditions. Regulatory T cells are one of the key cells to maintaining immune homeostasis. The age‐dependent changes in CD4 + Foxp3 + regulatory T cells (Tregs) have been well documented. However, the nonredundant Foxp3 − CD8 + Tregs were never examined in the context of aging. This study first established clear distinctions between phenotypically overlapping CD8 + Tregs and virtual memory T cells. Then, we elucidated the dynamics of CD8 + Tregs across the lifespan in mice and further extended our investigation to human peripheral blood mononuclear cells (PBMCs). In mice, we discovered a bi‐phasic dynamic shift in the frequency of CD8 + CD44 hi CD122 hi Ly49 + Tregs, with a steady increase in young adults and a notable peak in middle age followed by a decline in older mice. Transcriptomic analysis revealed that mouse CD8 + Tregs upregulated a selected set of natural killer (NK) cell‐associated genes, including NKG2D, with age. Importantly, NKG2D might negatively regulate CD8 + Tregs. Additionally, by analyzing a scRNA‐seq dataset of human PBMC, we found a distinct CD8 + Treg‐like subset (Cluster 10) with comparable age‐dependent frequency changes and gene expression, suggesting a conserved aging pattern in CD8 + Treg across mice and humans. In summary, our findings highlight the importance of CD8 + Tregs in immune regulation and aging.","journal":"Aging Cell","year":2024,"id":456943,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9541,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":314395,"name":"Shruti Mishra","orcid":"0000-0001-8055-1475","position":1,"is_corresponding":false},{"id":725837,"name":"Kenneth Fan","orcid":"0000-0002-1951-1244","position":2,"is_corresponding":false},{"id":725836,"name":"Liwen Wang","orcid":"0000-0003-1239-4927","position":3,"is_corresponding":false},{"id":231702,"name":"John Im","orcid":"0000-0001-6939-4851","position":4,"is_corresponding":false},{"id":1282290,"name":"Courtney Segura","orcid":null,"position":5,"is_corresponding":false},{"id":715860,"name":"Neelam Mukherjee","orcid":"0000-0003-1974-6075","position":6,"is_corresponding":false},{"id":1236758,"name":"Gang Huang","orcid":"0000-0002-8692-7856","position":7,"is_corresponding":false},{"id":640937,"name":"Manjeet K. Rao","orcid":"0000-0001-7573-2677","position":8,"is_corresponding":false},{"id":315978,"name":"Chaoyu Ma","orcid":null,"position":9,"is_corresponding":false},{"id":256588,"name":"Nu Zhang","orcid":"0000-0001-9695-210X","position":10,"is_corresponding":false},{"id":634922,"name":"Saranya Srinivasan","orcid":"0000-0002-9039-8635","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:03:32.516678Z","pmid":"39696807","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}