Molecular Dissection of CD8 + T-Cell Dysfunction

Wang, Chao; Singer, Meromit; Anderson, Ana C.

doi:10.1016/j.it.2017.05.008

Cited by 50 publications

(36 citation statements)

References 63 publications

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“…Multiple recent studies of TCE have noted the existence of distinct 'reversible' and 'irreversible' subpopulations of exhausted CD8 + T cells [10][11][12] . Reversibly exhausted CD8 + T cells have been variously defined by high expression levels of CXCR5, TCF-1, and BCL6, and concurrent low expression of KLRG1, BLIMP-1 (PRDM1), and TIM3 (HAVCR2) 25 . They have relatively high proliferative potential and can produce fully functional memory cells 25 .…”

Section: Genes In the Tce Network Can Be Divided Into Early And Late mentioning

confidence: 99%

“…Reversibly exhausted CD8 + T cells have been variously defined by high expression levels of CXCR5, TCF-1, and BCL6, and concurrent low expression of KLRG1, BLIMP-1 (PRDM1), and TIM3 (HAVCR2) 25 . They have relatively high proliferative potential and can produce fully functional memory cells 25 . In contrast, irreversibly exhausted CD8 + T cells are defined by the opposite expression pattern and have low proliferative and memory-forming potential.…”

Section: Genes In the Tce Network Can Be Divided Into Early And Late mentioning

confidence: 99%

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Integrative network modeling reveals mechanisms underlying T cell exhaustion

Bolouri

Young²,

Beilke³

et al. 2019

Preprint

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Failure to clear antigens causes CD8 + T cells to become increasingly hypo-functional, a state known as exhaustion. We combined manually extracted information from published literature with gene expression data from diverse model systems to infer a set of molecular regulatory interactions that underpin exhaustion. Topological analysis and simulation modeling of the network suggests CD8 + T cells undergo 2 major transitions in state following stimulation. The time cells spend in the earlier proliferative/pro-memory (PP) state is a fixed and inherent property of the network structure. Transition to the second state is necessary for exhaustion.Combining insights from network topology analysis and simulation modeling, we predict the extent to which each node in our network drives cells towards an exhausted state. We demonstrate the utility of our approach by experimentally testing the prediction that druginduced interference with EZH2 function increases the proportion of proliferative/pro-memory cells in the early days post-activation. Evaluation of cellular metabolic activity using transcriptional signaturesA set of marker regulatory genes per pathway were manually derived to explore metabolic changes in activated and exhausted CD8 + cells. Metabolic pathways typically comprise 3 topological features: metabolic transformation chains, incoming (joining) paths, and outgoing (forking) paths. We focused on marker genes that regulate metabolic transformation chains, because such genes are more likely to be highly correlated.

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Section: Genes In the Tce Network Can Be Divided Into Early And Late mentioning

confidence: 99%

Section: Genes In the Tce Network Can Be Divided Into Early And Late mentioning

confidence: 99%

Integrative network modeling reveals mechanisms underlying T cell exhaustion

Bolouri

Young²,

Beilke³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…To detect conserved and differentially expressed genes markers across datasets, differential expression testing was performed on the integrated datasets from all five samples. By performing these two steps, further information was obtained about exhaustion markers, and dot plots were generated 11 to display conserved and differentially expressed gene markers.…”

Section: Hiv-infected Donorsmentioning

confidence: 99%

“…In addition, CD8 + T cell exhaustion prevents the differentiation of effector cells into memory cells with the ability to undergo rapid reactivation upon encounter with antigen 7,8 . Chronically activated CD4 + T cells also lose their effector functions, including the production of cytokines, such as IL-2 and IL-21, that sustain HIV-specific CD8 + T cells [9][10][11] .…”

mentioning

confidence: 99%

An Atlas of Immune Cell Exhaustion in HIV-Infected Individuals Revealed by Single-Cell Transcriptomics

Wang

Zhang

Hui

et al. 2019

Preprint

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Chronic infection with human immunodeficiency virus (HIV) can cause progressive loss of immune cell function, or exhaustion, which impairs control of virus replication. However, little is known about the development and maintenance, as well as heterogeneity of immune cell exhaustion. Here, we investigated the effects of HIV infection on immune cell exhaustion at the transcriptomic level by analyzing single-cell RNA sequencing of peripheral blood mononuclear cells from two healthy subjects (15,121 cells) and six HIV-infected donors (28,610 cells). We identified nine immune cell clusters and eight T cell subclusters according to their unique gene expression programs; three of these (exhausted CD4 + and CD8 + T cells and interferonresponsive CD8 + T cells) were detected only in samples from HIV-infected donors. An inhibitory receptor KLRG1 was identified in the exhausted T cell populations and further characterized in HIV infected individuals. We identified a novel HIV-1 specific exhausted CD8 + T cell population expressing KLRG1, TIGIT, and T-bet dim Eomes hi markers. Ex-vivo antibody blockade of KLRG1 restored the function of HIV-specific exhausted CD8 + T cells demonstrating the contribution of KLRG1 + population to T cell exhaustion and providing a novel target for developing immunotherapy to treat HIV chronic infection. Analysis of gene signatures also revealed impairment of B cell and NK cell function in HIVinfected donors. These data provide a comprehensive analysis of gene signatures associated with immune cell exhaustion during HIV infection, which could be useful in understanding exhaustion mechanisms and developing new cure therapies. We thank Drs. Doug Richman, Mario Stevenson, and Jon Karn for helpful discussions, Kristen Jepsen at the IGM Genomics Center for help with scRNA-seq, Celsa Spina of UCSD CFAR for flow analysis and members of the Rana lab for helpful discussions and advice. We also thank Dr. Song Chen for advice and help in sample preparation and data analysis.

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“…Understanding the origins of exhaustion and devising ways of preventing or reversing it form areas of intense focus today, as they bring promise of identifying novel avenues of intervention for diseases that may otherwise be incurable (Pardoll, 2012;Wykes & Lewin, 2018). Indeed, remarkable progress has been made in the recent years in characterizing the exhausted state and its implications for disease progression (Kahan et al, 2015;Wang, Singer, & Anderson, 2017;Wherry, 2011;Wherry & Kurachi, 2015).…”

Section: Cd8 + T Cell Exhaustionmentioning

confidence: 99%

Towards multiscale modeling of the CD8⁺T cell response to viral infections

Baral

Raja

Sen

et al. 2019

WIREs Mechanisms of Disease

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The CD8 + T cell response is critical to the control of viral infections. Yet, defining the CD8 + T cell response to viral infections quantitatively has been a challenge. Following antigen recognition, which triggers an intracellular signaling cascade, CD8 + T cells can differentiate into effector cells, which proliferate rapidly and destroy infected cells. When the infection is cleared, they leave behind memory cells for quick recall following a second challenge. If the infection persists, the cells may become exhausted, retaining minimal control of the infection while preventing severe immunopathology. These activation, proliferation and differentiation processes as well as the mounting of the effector response are intrinsically multiscale and collective phenomena. Remarkable experimental advances in the recent years, especially at the single cell level, have enabled a quantitative characterization of several underlying processes. Simultaneously, sophisticated mathematical models have begun to be constructed that describe these multiscale phenomena, bringing us closer to a comprehensive description of the CD8 + T cell response to viral infections. Here, we review the advances made and summarize the challenges and opportunities ahead. This article is categorized under: Analytical and Computational Methods > Computational Methods Biological Mechanisms > Cell Fates Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models

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Molecular Dissection of CD8 + T-Cell Dysfunction

Cited by 50 publications

References 63 publications

Integrative network modeling reveals mechanisms underlying T cell exhaustion

Integrative network modeling reveals mechanisms underlying T cell exhaustion

An Atlas of Immune Cell Exhaustion in HIV-Infected Individuals Revealed by Single-Cell Transcriptomics

Towards multiscale modeling of the CD8⁺T cell response to viral infections

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Molecular Dissection of CD8 + T-Cell Dysfunction

Cited by 50 publications

References 63 publications

Integrative network modeling reveals mechanisms underlying T cell exhaustion

Integrative network modeling reveals mechanisms underlying T cell exhaustion

An Atlas of Immune Cell Exhaustion in HIV-Infected Individuals Revealed by Single-Cell Transcriptomics

Towards multiscale modeling of the CD8+T cell response to viral infections

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Towards multiscale modeling of the CD8⁺T cell response to viral infections