scholarly journals Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages

2021 ◽  
Vol 12 ◽  
Author(s):  
Qiyu Zhong ◽  
Fan Yang ◽  
Xiaochuan Chen ◽  
Jinbo Li ◽  
Cailing Zhong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Plasma Cells ◽  
Immune Cell ◽  
M2 Macrophages ◽  
Expression Data ◽  
Gene Expression Microarray Data ◽  
And Function ◽  
Ovarian Endometriomas ◽  
Activation Status

Background: Endometriosis (EMS) is an estrogen-dependent disease in which endometrial glands and stroma arise outside the uterus. Current studies have suggested that the number and function of immune cells are abnormal in the abdominal fluid and ectopic lesion tissues of patients with EMS. The developed CIBERSORT method allows immune cell profiling by the deconvolution of gene expression microarray data.Methods: By applying CIBERSORT, we assessed the relative proportions of immune cells in 68 normal endometrial tissues (NO), 112 eutopic endometrial tissues (EU) and 24 ectopic endometrial tissues (EC). The obtained immune cell profiles provided enumeration and activation status of 22 immune cell subtypes. We obtained associations between the immune cell environment and EMS r-AFS stages. Macrophages were evaluated by immunohistochemistry (IHC) in 60 patients with ovarian endometriomas.Results: Total natural killer (NK) cells were significantly decreased in EC, while plasma cells and resting CD4 memory T cells were increased in EC. Total macrophages in EC were significantly increased compared to those of EU and NO, and M2 macrophages were the primary macrophages in EC. Compared to those of EC from patients with r-AFS stage I ~ II, M2 macrophages in EC from patients with stage III ~ IV were significantly increased. IHC experiments showed that total macrophages were increased in EC, with M2 macrophages being the primary subtype.Conclusions: Our data demonstrate that deconvolution of gene expression data by CIBERSORT provides valuable information about immune cell composition in EMS.

scholarly journals Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

2021 ◽  
Vol 11 ◽  
Author(s):  
Min Qin ◽  
Zhihai Liang ◽  
Heping Qin ◽  
Yifang Huo ◽  
Qing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regression Analysis ◽  
Immune Cells ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Immune Cell ◽  
Risk Group ◽  
P Value ◽  
Expression Data

IntroductionGastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.Materials and MethodsGene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.ResultsAfter five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.ConclusionCGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.

scholarly journals The Immune Cell Landscape in Different Anatomical Structures of Knee in Osteoarthritis: A Gene Expression-Based Study

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Ziming Chen ◽  
Yuanchen Ma ◽  
Xuerui Li ◽  
Zhantao Deng ◽  
Minghao Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Mast Cells ◽  
Subchondral Bone ◽  
Immune Cells ◽  
Immune Cell ◽  
M2 Macrophages ◽  
Anatomical Structures ◽  
Normal Tissues ◽  
Immunological Mechanisms

Background. Immunological mechanisms play a vital role in the pathogenesis of knee osteoarthritis (KOA). Moreover, the immune phenotype is a relevant prognostic factor in various immune-related diseases. In this study, we used CIBERSORT for deconvolution of global gene expression data to define the immune cell landscape of different structures of knee in osteoarthritis. Methods and Findings. By applying CIBERSORT, we assessed the relative proportions of immune cells in 76 samples of knee cartilage, 146 samples of knee synovial tissue, 40 samples of meniscus, and 50 samples of knee subchondral bone. Enumeration and activation status of 22 immune cell subtypes were provided by the obtained immune cell profiles. In synovial tissues, the differences in proportions of plasma cells, M1 macrophages, M2 macrophages, activated dendritic cells, resting mast cells, and eosinophils between normal tissues and osteoarthritic tissues were statistically significant (P<0.05). The area under the curve was relatively large in resting mast cells, dendritic cells, and M2 macrophages in receiver operating characteristic analyses. In subchondral bones, the differences in proportions of resting master cells and neutrophils between normal tissues and osteoarthritic tissues were statistically significant (P<0.05). In subchondral bones, the proportions of immune cells, from the principle component analyses, displayed distinct group-bias clustering. Resting mast cells and T cell CD8 were the major component of first component. Moreover, we revealed the potential interaction between immune cells. There was almost no infiltration of immune cells in the meniscus and cartilage of the knee joint. Conclusions. The immune cell composition in KOA differed substantially from that of healthy joint tissue, while it also differed in different anatomical structures of the knee. Meanwhile, activated mast cells were mainly associated with high immune cell infiltration in OA. Furthermore, we speculate M2 macrophages in synovium and mast cells in subchondral bone may play an important role in the pathogenesis of OA.

scholarly journals Elucidating the immune infiltration in acne and its comparison with rosacea by integrated bioinformatics analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248650
Author(s):  
Lu Yang ◽  
Yan-Hong Shou ◽  
Yong-Sheng Yang ◽  
Jin-Hua Xu
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Mast Cells ◽  
Immune Cells ◽  
Plasma Cells ◽  
Immune Cell ◽  
Healthy Individuals ◽  
Lesional Skin ◽  
Immune Infiltration ◽  
Acne Rosacea

Background Acne vulgaris and rosacea are common inflammatory complications of the skin, both characterized by abnormal infiltration of immune cells. The two diseases can be differentiated based on characteristic profile of the immune cell infiltrates at the periphery of disease lesions. In addition, dysregulated infiltration of immune cells not only occur in the acne lesions but also in non-lesional areas of patients with the disease, thus characterizing the immune infiltration in these sites can further enhance our understanding on the pathogenesis of acne. Methods Five microarray data-sets (GSE108110, GSE53795, GSE65914, GSE14905 and GSE78097) were downloaded from Gene Expression Omnibus. After removing the batch effects and normalizing the data, we applied the CIBERSORT algorithm combined with signature matrix LM22, to describe 22 types of immune cells’ infiltration in acne less than 48 hour (H) old, in comparation with non-lesional skin of acne patients, healthy skin and rosacea (including erythematotelangiectatic rosacea, papulopustular rosacea and phymatous rosacea) and we compared gene expression of Th1 and Th17-related molecules in acne, rosacea and healthy control. Results Compared with the non-lesional skin of acne patients, healthy individuals and rosacea patients, there is a significant increase in infiltration of neutrophils, monocytes and activated mast cells around the acne lesions, less than 48 H after their development. Contrarily, few naive CD4+ T cells, plasma cells, memory B cells and resting mast cells infiltrate acne sites compared to the aforementioned groups of individuals. Moreover, the infiltration of Regulatory T cells (Tregs) in acne lesions is substantially lower, relative to non-lesional sites of acne patients and skin of healthy individuals. In addition, non-lesional sites of acne patients exhibit lower infiltration of activated memory CD4+ T cells, plasma cells, memory B cells, M0 macrophages, neutrophils, resting mast cells but higher infiltration of Tregs and resting dendritic cells relative to skin of healthy individuals. Intriguingly, we found that among the 3 rosacea subtypes, the immune infiltration profile of papulopustular rosacea is the closest to that of acne lesions. In addition, through gene expression analysis of acne, rosacea and skin tissues of healthy individuals, we found a higher infiltration of Th1 and Th17 cells in acne lesions, relative to non-lesional skin areas of acne patients. Conclusions Our study provides new insights into the inflammatory pathogenesis of acne, and the difference between acne and rosacea, which helps in differentiating the two diseases. Our findings also guide on appropriate target therapy of the immune cell infiltrates in the two disease conditions.

Resting memory CD4+ T cells and plasma cells in gastric cancer may be potential immune biomarkers.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16523-e16523
Author(s):  
Jingde Chen ◽  
Bei Zhang ◽  
Yifan Zhou ◽  
Xiaochen Zhao ◽  
Yuezong Bai
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cells ◽  
Plasma Cells ◽  
Immune Cell ◽  
Expression Data ◽  
Immune Biomarkers ◽  
Memory Cd4 T Cells

e16523 Background: Previous studies revealed the CD8+T cells deeply involved in tumor progress and reaction to immunotherapy. Now we used expression data and in silicon algorithm to analyze immune cell composition in gastric cancer and explored other subsets of immune cells that may be the potential immune biomarkers. Methods: CIBERSORT quantified 22 immune cell subtypes using four GEO gastric cancer cohorts (GSE15459, GSE26253, GSE29272, GSE57303) and TCGA-STAD, TCGA-PAAD gene expression data, and only CIBERSORT P- value of < 0.05 were included in the survival analysis. Immune cell high was defined as ≥median cells proportion individually and were computed for survival analysis and hazard ratios. Wilcox test was applied to analyze the differences between normal and tumor tissues. Log-rank Mantel-Cox test was applied to compare the survival curves between the patient groups. Statistical analyses were conducted using R v3.3.2. Results: Unsupervised hierarchical analysis of four GEO gastric tumor cohort using 22 immune cells proportions identifies three subclasses. In the subclass with the best overall survival performance, we found enriched resting CD4 T memory cells. Then in TCGA-STAD (P = 0.03) and TCGA-PAAD (P = 0.03) cohort, we observed that normal tissue obtained higher fraction of resting CD4 T memory cells than tumor. On the other hand, in patients who administrated chemotherapy in TCGA-STAD, by comparing immune cell high and low subgroup, we found that plasma cells (P = 0.02), T cells CD8(P = 0.03) were associated with improved overall survival. while, neutrophils(P = 0.05), NK cells resting(P = 0.04), were correlated with decreased overall survival. Also we proved one similar result in GEO cohort, that plasma cells-high (above median) subgroup provided increased overall survival (P = 0.04, HR = 0.76). In contrast, other three immune cells were not significantly associated with survival benefits. Conclusions: Together, these results indicated that memory CD4 T cells and plasma cells infiltration in gastric cancer have important clinical meanings and may be potential immune biomarkers. The continued integration of observations from a variety of experimental models will be required to further understand and utilize the full potential of immune cells.

scholarly journals How Changes in the Nutritional Landscape Shape Gut Immunometabolism

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 823
Author(s):  
Jian Tan ◽  
Duan Ni ◽  
Rosilene V. Ribeiro ◽  
Gabriela V. Pinget ◽  
Laurence Macia
Keyword(s):  
Immune Cells ◽  
Metabolic Pathways ◽  
Immune Cell ◽  
Food Additives ◽  
Cell Activity ◽  
Inflammatory Cells ◽  
Therapeutic Approaches ◽  
Food Antigens ◽  
And Function ◽  
Micro Organisms

Cell survival, proliferation and function are energy-demanding processes, fuelled by different metabolic pathways. Immune cells like any other cells will adapt their energy production to their function with specific metabolic pathways characteristic of resting, inflammatory or anti-inflammatory cells. This concept of immunometabolism is revolutionising the field of immunology, opening the gates for novel therapeutic approaches aimed at altering immune responses through immune metabolic manipulations. The first part of this review will give an extensive overview on the metabolic pathways used by immune cells. Diet is a major source of energy, providing substrates to fuel these different metabolic pathways. Protein, lipid and carbohydrate composition as well as food additives can thus shape the immune response particularly in the gut, the first immune point of contact with food antigens and gastrointestinal tract pathogens. How diet composition might affect gut immunometabolism and its impact on diseases will also be discussed. Finally, the food ingested by the host is also a source of energy for the micro-organisms inhabiting the gut lumen particularly in the colon. The by-products released through the processing of specific nutrients by gut bacteria also influence immune cell activity and differentiation. How bacterial metabolites influence gut immunometabolism will be covered in the third part of this review. This notion of immunometabolism and immune function is recent and a deeper understanding of how lifestyle might influence gut immunometabolism is key to prevent or treat diseases.

scholarly journals Predicting Host Immune Cell Dynamics and Key Disease-Associated Genes Using Tissue Transcriptional Profiles

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 301
Author(s):  
Muying Wang ◽  
Satoshi Fukuyama ◽  
Yoshihiro Kawaoka ◽  
Jason E. Shoemaker
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Mean Squared Error ◽  
Expression Profiles ◽  
Statistical Tests ◽  
Critical Factor ◽  
Expression Data ◽  
Cell Dynamics ◽  
Cell Counts

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests.

scholarly journals When Cells Suffocate: Autophagy in Cancer and Immune Cells under Low Oxygen

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Katrin Schlie ◽  
Jaeline E. Spowart ◽  
Luke R. K. Hughson ◽  
Katelin N. Townsend ◽  
Julian J. Lum
Keyword(s):  
Tumor Microenvironment ◽  
Cancer Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Treatment Strategies ◽  
Patient Treatment ◽  
Low Oxygen ◽  
Tumor Environment ◽  
And Function ◽  
The Impact

Hypoxia is a signature feature of growing tumors. This cellular state creates an inhospitable condition that impedes the growth and function of all cells within the immediate and surrounding tumor microenvironment. To adapt to hypoxia, cells activate autophagy and undergo a metabolic shift increasing the cellular dependency on anaerobic metabolism. Autophagy upregulation in cancer cells liberates nutrients, decreases the buildup of reactive oxygen species, and aids in the clearance of misfolded proteins. Together, these features impart a survival advantage for cancer cells in the tumor microenvironment. This observation has led to intense research efforts focused on developing autophagy-modulating drugs for cancer patient treatment. However, other cells that infiltrate the tumor environment such as immune cells also encounter hypoxia likely resulting in hypoxia-induced autophagy. In light of the fact that autophagy is crucial for immune cell proliferation as well as their effector functions such as antigen presentation and T cell-mediated killing of tumor cells, anticancer treatment strategies based on autophagy modulation will need to consider the impact of autophagy on the immune system.

scholarly journals Gut symbiotic microbes imprint intestinal immune cells with the innate receptor SLAMF4 which contributes to gut immune protection against enteric pathogens

Gut ◽  
2017 ◽  
Vol 67 (5) ◽  
pp. 847-859 ◽  
Author(s):  
Allison Cabinian ◽  
Daniel Sinsimer ◽  
May Tang ◽  
Youngsoon Jang ◽  
Bongkum Choi ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Immune Cells ◽  
Immune Cell ◽  
Lymphocyte Activation ◽  
Premature Death ◽  
Enteric Pathogens ◽  
Immune Protection ◽  
Intestinal Immunity ◽  
Cell Responses ◽  
And Function

BackgroundInteractions between host immune cells and gut microbiota are crucial for the integrity and function of the intestine. How these interactions regulate immune cell responses in the intestine remains a major gap in the field.AimWe have identified the signalling lymphocyte activation molecule family member 4 (SLAMF4) as an immunomodulator of the intestinal immunity. The aim is to determine how SLAMF4 is acquired in the gut and what its contribution to intestinal immunity is.MethodsExpression of SLAMF4 was assessed in mice and humans. The mechanism of induction was studied using GFPtg bone marrow chimaera mice, lymphotoxin α and TNLG8A-deficient mice, as well as gnotobiotic mice. Role in immune protection was revealed using oral infection with Listeria monocytogenes and Cytobacter rodentium.ResultsSLAMF4 is a selective marker of intestinal immune cells of mice and humans. SLAMF4 induction occurs directly in the intestinal mucosa without the involvement of the gut-associated lymphoid tissue. Gut bacterial products, particularly those of gut anaerobes, and gut-resident antigen-presenting cell (APC)TNLG8A are key contributors of SLAMF4 induction in the intestine. Importantly, lack of SLAMF4 expression leads the increased susceptibility of mice to infection by oral pathogens culminating in their premature death.ConclusionsSLAMF4 is a marker of intestinal immune cells which contributes to the protection against enteric pathogens and whose expression is dependent on the presence of the gut microbiota. This discovery provides a possible mechanism for answering the long-standing question of how the intertwining of the host and gut microbial biology regulates immune cell responses in the gut.

scholarly journals The Toolbox for Untangling Chromosome Architecture in Immune Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuai Liu ◽  
Keji Zhao
Keyword(s):  
Gene Expression ◽  
Immune Cells ◽  
Spatial Organization ◽  
Chromosome Structure ◽  
Immune Cell ◽  
Genome Structure ◽  
Chromosome Architecture ◽  
The Past ◽  
Important Player ◽  
The Way

The code of life is not only encrypted in the sequence of DNA but also in the way it is organized into chromosomes. Chromosome architecture is gradually being recognized as an important player in regulating cell activities (e.g., controlling spatiotemporal gene expression). In the past decade, the toolbox for elucidating genome structure has been expanding, providing an opportunity to explore this under charted territory. In this review, we will introduce the recent advancements in approaches for mapping spatial organization of the genome, emphasizing applications of these techniques to immune cells, and trying to bridge chromosome structure with immune cell activities.

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