Dataset Information

DNA accessibility in reactive and non-reactive CMV specific CD8+ lymphocytes

ABSTRACT: Poor immune reconstitution following haematopoietic stem cell transplantation (HSCT) can cause unwanted and harmful sequelae in recipients with chronic cytomegalovirus (CMV) infection. To understand the molecular mechanisms underlying poor reconstitution, we profiled the transcriptome-chromatin accessibility landscape of CMV-specific CD8+ memory lymphocytes from HCST recipients with different reconstitution efficiencies. Although the transcriptional and epigenomic signatures in non-reactive lymphocytes were not naïve or classic ‘exhaustion’ profiles, some gene expression was common to lymphocytes from other non-resolving infections. Reactive lymphocytes expressed higher levels of interferon/defence response and cell cycle genes in an interconnected network involving PI3KCG, STAT5B, NFAT, RBPJ, and NR3C1, increasing chromatin accessibility at the enhancer regions of immune and T cell receptor signalling pathway genes. We propose that increased HDAC6 expression in non-reactive lymphocytes decreases accessibility at particular enhancers including PI3KCG. Non-reactive cells also expressed higher levels of EGR and KLF factors that, along with lower expression of JARID2, maintained higher accessibility at promoter and CpG-rich regions of genes associated with apoptosis. Together, transcription factors and chromatin modulators create a different chromatin accessibility landscape in reactive and non-reactive cells that not only affects immediate gene expression but differentially primes the cells for responses to additional signals.

ORGANISM(S): Homo sapiens

PROVIDER: GSE104750 | GEO | 2018/04/09

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Measurement of specific gene expression in clinical samples is a promising approach for monitoring the recipient immune status to the graft in organ transplantation. Identification of biomarker genes closely associated with tolerance or rejection is critical for this monitoring protocol. Unlike previous studies, our microarray analysis focused on donor antigen-reactive T cells, which were prepared by collecting CD69+ T cells from cocultures of recipient peripheral T cells and donor antigen-presenting cells. A comparison of different recipient groups enabled us to identify several tolerance- and rejection-correlated biomarker genes, including previously unknown genes. By measuring biomarker gene expression in the CD69+ T cell fraction using quantitative reverse-transcription polymerase chain reaction, we were able to precisely detect the immune status of recipients relative to their graft. Full-thickness donor (BDF1; C57BL/6 x DBA/2) tail skin was grafted onto recipient (C57BL/6) mice. Tolerance to the graft was induced by donor specific transfusion combined with administration of anti-CD40L Ab (MR-1). Total T lymphocytes were prepared from spleen and lymph nodes of tolerant recipient mice on day 100 after skin transplantation, untreated recipient mice on day 7, and ungrafted, healthy C57BL/6 mice and used as tolerant, rejecting and naïve T lymphocyte samples, respectively. In order to isolate alloantigen-reactive cells, T lymphocytes were cocultured with donor T cell-depleted splenocytes for 20-21 hours and then separated into two subpopulations based on CD69 expression using fluorescence cell sorter. Total RNA was extracted from these CD69+ and CD69- T cells and subjected to microarray using Illunima MouseWG-6 Expression BeadChip. All data analysis and visualization of differentially expressed genes was conducted using Illumina GenomeStudio v2009.2 (Gene Expression Module v1.5.4) and R statistical language v2.6.1. Candidate tolerance genes were defined as the common genes satisfying the following criteria; 1) 1.5 fold or higher expression in CD69+ T cells isolated from tolerant recipients than those from naïve mice, 2) 1.5 fold or higher expression in CD69+ T cells than CD69- T cells from tolerant mice, 3) 500 or higher average signal measured in CD69+ T cells from tolerant mice. The criteria for candidate rejection genes were 1) 1.5 fold or higher expression in CD69+ T cells isolated from rejecting recipients than those from tolerant mice, 2) 1.5 fold or higher expression in CD69+ T cells than CD69- T cells from rejecting mice, 3) 500 or higher average signal measured in CD69+ T cells from rejecting mice.

Project description:Measurement of specific gene expression in clinical samples is a promising approach for monitoring the recipient immune status to the graft in organ transplantation. Identification of biomarker genes closely associated with tolerance or rejection is critical for this monitoring protocol. Unlike previous studies, our microarray analysis focused on donor antigen-reactive T cells, which were prepared by collecting CD69+ T cells from cocultures of recipient peripheral T cells and donor antigen-presenting cells. A comparison of different recipient groups enabled us to identify several tolerance- and rejection-correlated biomarker genes, including previously unknown genes. By measuring biomarker gene expression in the CD69+ T cell fraction using quantitative reverse-transcription polymerase chain reaction, we were able to precisely detect the immune status of recipients relative to their graft. Full-thickness donor (BDF1; C57BL/6 x DBA/2) tail skin was grafted onto recipient (C57BL/6) mice. Tolerance to the graft was induced by donor specific transfusion combined with administration of anti-CD40L Ab (MR-1). Total T lymphocytes were prepared from spleen and lymph nodes of tolerant recipient mice on day 21 and 100 after skin transplantation, untreated recipient mice on day 7, and ungrafted, healthy C57BL/6 mice and used as early tolerant, tolerant, rejecting and naïve T lymphocyte samples, respectively. In order to isolate alloantigen-reactive cells, T lymphocytes were cocultured with donor BM-DCs for 20-21 hours and then separated into two subpopulations based on CD69 expression using fluorescence cell sorter. Total RNA was extracted from these CD69+ and CD69- T cells and subjected to microarray using Illunima MouseRef-8 Expression BeadChip. All data analysis and visualization of differentially expressed genes was conducted using Illumina BeadStudio v3.1.3 (Gene Expression Module v3.3.8), Array Assist 5.5.1 (Stratagene) and R 2.4.1(www.r-project.org). Candidate tolerance genes were defined as the common genes satisfying the following criteria; 1) 1.5 fold or higher expression in CD69+ T cells isolated from tolerant recipients than those from naïve mice, 2) 1.5 fold or higher expression in CD69+ T cells than CD69- T cells from tolerant mice, 3) 500 or higher average signal measured in CD69+ T cells from tolerant mice. The criteria for candidate rejection genes were 1) 1.5 fold or higher expression in CD69+ T cells isolated from rejecting recipients than those from tolerant mice, 2) 1.5 fold or higher expression in CD69+ T cells than CD69- T cells from rejecting mice, 3) 500 or higher average signal measured in CD69+ T cells from rejecting mice.

Dataset Information

DNA accessibility in reactive and non-reactive CMV specific CD8+ lymphocytes

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets