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Modeling the dynamics of neonatal CD8+ T-cell responses.

ABSTRACT: Neonates are particularly susceptible to a number of infections, and the neonatal CD8+ T-cell response demonstrates differences in both the phenotype and magnitude of responses to infection compared with adults. However, the underlying basis for these differences is unclear. We have used a mathematical modeling approach to analyze the dynamics of neonatal and adult CD8+ T-cell responses following in vitro stimulation and in vivo infection, which allows us to dissect key cell-intrinsic differences in expansion, differentiation and memory formation. We found that neonatal cells started dividing 8?h earlier and proliferated at a faster rate (0.077 vs 0.105 per day) than adult cells in vitro. In addition, neonatal cells also differentiated more rapidly, as measured by the loss in CD62L and Ly6C expression. We extended our mathematical modeling to analysis of neonatal and adult CD8+ T cells responding in vivo and demonstrated that neonatal cells divide more slowly than adult cells after day 4 post infection. However, neonatal cells differentiate more rapidly, upregulating more KLRG1 per division than adult cells (20% vs 5%). The dynamics of memory formation were also found to be different, with neonatal effector cells showing increased death (1.0 vs 2.45 per day). Comparison of the division of human cord blood and adult naive cells stimulated in vitro showed more division in cord blood-derived cells, consistent with the observations in mice. This work highlights differences of the cell-intrinsic division and differentiation program in neonatal CD8+ T cells.

SUBMITTER: Reynaldi A

PROVIDER: S-EPMC5069106 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Modeling the dynamics of neonatal CD8+ T-cell responses.

Reynaldi Arnold A Smith Norah L NL Schlub Timothy E TE Venturi Vanessa V Rudd Brian D BD Davenport Miles P MP

Immunology and cell biology 20160504 9

Neonates are particularly susceptible to a number of infections, and the neonatal CD8+ T-cell response demonstrates differences in both the phenotype and magnitude of responses to infection compared with adults. However, the underlying basis for these differences is unclear. We have used a mathematical modeling approach to analyze the dynamics of neonatal and adult CD8+ T-cell responses following in vitro stimulation and in vivo infection, which allows us to dissect key cel ...[more]

PMID: 27142943

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Project description:BackgroundThe tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.MethodUtilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.ResultsOur study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.ConclusionThe creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

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Modeling the dynamics of neonatal CD8+ T-cell responses.

Publications

Modeling the dynamics of neonatal CD8<sup>+</sup> T-cell responses.

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