Comparison of gene expression profiles of T cells in porcine colostrum and peripheral blood

AbstractAntigen-inexperienced memory-like T (AIMT) cells are functionally unique T cells representing one of the two largest subsets of murine CD8+ T cells. However, differences between laboratory inbred strains, insufficient data from germ-free mice, a complete lack of data from feral mice, and unclear relationship between AIMT cells formation during aging represent major barriers for better understanding of their biology. We performed a thorough characterization of AIMT cells from mice of different genetic background, age, and hygienic status by flow cytometry and multi-omics approaches including analyses of gene expression, TCR repertoire, and microbial colonization. Our data showed that AIMT cells are steadily present in mice independently of their genetic background and hygienic status. Despite differences in their gene expression profiles, young and aged AIMT cells originate from identical clones. We identified that CD122 discriminates two major subsets of AIMT cells in a strain-independent manner. Whereas thymic CD122LOW AIMT cells (innate memory) prevail only in young animals with high thymic IL-4 production, peripheral CD122HIGH AIMT cells (virtual memory) dominate in aged mice. Co-housing with feral mice changed the bacterial colonization of laboratory strains, but had only minimal effects on the CD8+ T-cell compartment including AIMT cells.

Download Full-text

Meta-analysis of gene expression profiles of peripheral blood cells in systemic lupus erythematosus

Cellular and Molecular Biology ◽

10.14715/cmb/2018.64.10.7 ◽

2018 ◽

Vol 64 (10) ◽

pp. 40 ◽

Cited By ~ 2

Author(s):

Sang-Cheol Bae ◽

Young Ho Lee

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Lupus Erythematosus ◽

Peripheral Blood ◽

Blood Cells ◽

Expression Profiles ◽

Meta Analysis ◽

Gene Expression Profiles ◽

Peripheral Blood Cells ◽

Systemic Lupus

Download Full-text

Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

10.21203/rs.3.rs-59448/v1 ◽

2020 ◽

Author(s):

Rui Zhang ◽

Chen Chen ◽

Qi Li ◽

Jialu Fu ◽

Dong Zhang ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

High Risk ◽

Risk Score ◽

Risk Model ◽

Predictive Accuracy ◽

Expression Profiles ◽

Gene Expression Profiles ◽

The Cancer Genome Atlas ◽

Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were signiﬁcantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

Download Full-text