Gene Set Enrichment Analysis of Bronchial Alveolar Lavage Fluid Identifies Key Innate Immune Pathways in Primary Graft Dysfunction after Lung Transplantation

2013 ◽  
Vol 32 (4) ◽  
pp. S41-S42 ◽  
Author(s):  
E. Cantu ◽  
D.J. Lederer ◽  
K.C. Meyer ◽  
R.K. Milewski ◽  
Y. Suzuki ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Lavage Fluid ◽  
Innate Immune ◽  
Gene Set Enrichment Analysis ◽  
Primary Graft Dysfunction ◽  
Graft Dysfunction ◽  
Gene Set Enrichment ◽  
Bronchial Alveolar Lavage Fluid ◽  
Immune Pathways ◽  
Bronchial Alveolar Lavage

scholarly journals Identification of Pro-Fibrotic Macrophage Populations by Single-Cell Transcriptomic Analysis in West Highland White Terriers Affected With Canine Idiopathic Pulmonary Fibrosis

2020 ◽  
Vol 11 ◽  
Author(s):  
Aline Fastrès ◽  
Dimitri Pirottin ◽  
Laurence Fievez ◽  
Alexandru-Cosmin Tutunaru ◽  
Géraldine Bolen ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Single Cell ◽  
Enrichment Analysis ◽  
Lavage Fluid ◽  
Gene Set Enrichment Analysis ◽  
Cell Populations ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Macrophage Populations

Canine idiopathic pulmonary fibrosis (CIPF) affects old dogs from the West Highland white terrier (WHWT) breed and mimics idiopathic pulmonary fibrosis (IPF) in human. The disease results from deposition of fibrotic tissue in the lung parenchyma causing respiratory failure. Recent studies in IPF using single-cell RNA sequencing (scRNA-seq) revealed the presence of profibrotic macrophage populations in the lung, which could be targeted for therapeutic purpose. In dogs, scRNA-seq was recently validated for the detection of cell populations in bronchoalveolar lavage fluid (BALF) from healthy dogs. Here we used the scRNA-seq to characterize disease-related heterogeneity within cell populations of macrophages/monocytes (Ma/Mo) in the BALF from five WHWTs affected with CIPF in comparison with three healthy WHWTs. Gene set enrichment analysis was also used to assess pro-fibrotic capacities of Ma/Mo populations. Five clusters of Ma/Mo were identified. Gene set enrichment analyses revealed the presence of pro-fibrotic monocytes in higher proportion in CIPF WHWTs than in healthy WHWTs. In addition, monocyte-derived macrophages enriched in pro-fibrotic genes in CIPF compared with healthy WHWTs were also identified. These results suggest the implication of Ma/Mo clusters in CIPF processes, although, further research is needed to understand their role in disease pathogenesis. Overexpressed molecules associated with pulmonary fibrosis processes were also identified that could be used as biomarkers and/or therapeutic targets in the future.

scholarly journals Growth Hormone Releasing Hormone Reduces Plasma Markers of Immune Activation and Hepatic Immune Pathways in Nonalcoholic Fatty Liver Disease

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A628-A629
Author(s):  
Takara Leah Stanley ◽  
Lindsay T Fourman ◽  
Lai Ping Wong ◽  
Ruslan Sadreyev ◽  
James T Billingsley ◽  
...  
Keyword(s):  
T Cell ◽  
Immune Function ◽  
Immune Activation ◽  
Enrichment Analysis ◽  
Fold Change ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Log2 Fold Change ◽  
Circulating Markers ◽  
Immune Pathways

Abstract Introduction: The GH/IGF-1 axis affects multiple metabolic pathways, and animal models demonstrate that it also modulates immune function. Little is known, however, regarding effects of augmenting GH secretion on immune function in humans. This study used proteomics and gene set enrichment analysis to assess effects of a GH releasing hormone (GHRH) analog, tesamorelin, on circulating immune markers and immune-related gene pathways in the liver in people with HIV (PWH) and NAFLD. We hypothesized that tesamorelin would decrease circulating markers of immune activation in conjunction with previously reported reductions in visceral fat and hepatic triglyceride. Methods: 92 biomarkers associated with immune function (Olink Immuno-Oncology panel) were measured in plasma samples from 61 PWH with NAFLD who participated in a double-blind, randomized, 12-month trial of tesamorelin versus identical placebo. Proteins differentially altered by tesamorelin at a false discovery rate < 0.1 were considered significantly changed. Gene set enrichment analysis targeted to immune pathways was subsequently performed on liver tissue from serial biopsies. Results: Compared to placebo, tesamorelin decreased circulating concentrations of 13 proteins, including four chemokines (C-C Motif Chemokine Ligands 3 [CCL3, effect size -0.38 Log2 fold change], 4 [CCL4, -0.36 Log2 fold change], and 13 [CCL13 or MCP4, -0.42 Log2 fold change] and interleukin-8 [-0.50 Log2 fold change]), two cytokines (interleukin-10 [-0.32 Log2 fold change] and cytokine stimulating factor 1 [-0.22 Log2 fold change]), and four T-cell associated molecules (CD8A [-0.37 Log2 fold change], Cytotoxic And Regulatory T Cell Molecule [CRTAM, -0.47 Log2 fold change], granzyme A [-0.53 Log2 fold change], and adhesion G protein-coupled receptor G1 [ADGRG1, -0.54 Log2 fold change]), as well as arginase-1 [-0.95 Log2 fold change], galectin-9 [-0.26 Log2 fold change], and hepatocyte growth factor [-0.30 Log2 fold change]. No proteins in the panel were significantly increased by tesamorelin. Network analysis indicated close interaction among the gene pathways responsible for the reduced proteins, with imputational analyses suggesting down regulation of a closely related cluster of immune pathways. Targeted transcriptomics using tissue from liver biopsy confirmed an end-organ signal of down-regulated immune pathways, including pathways involved in antigen presentation, complement activation, toll like receptor and inflammatory signaling, and T-cell activation. Conclusions: Long-term treatment with tesamorelin decreased circulating markers of T-cell and monocyte/macrophage activity, with corresponding downregulation of immune pathways in the liver. These findings suggest that augmenting pulsatile GH may ameliorate immune activation in a population with metabolic dysregulation and systemic inflammation.

Identification of key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Wei Han ◽  
Dongchen Lu ◽  
Chonggao Wang ◽  
Mengdi Cui ◽  
Kai Lu
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Integrated Analysis ◽  
Papillary Thyroid ◽  
Gene Set Enrichment ◽  
Mirna Expression Data

Background: In the past decades, the incidence of thyroid cancer (TC) has been gradually increasing, owing to the widespread use of ultrasound scanning devices. However, the key mRNAs, miRNAs, and mRNA-miRNA network in papillary thyroid carcinoma (PTC) has not been fully understood. Material and Methods: In this study, multiple bioinformatics methods were employed, including differential expression analysis, gene set enrichment analysis, and miRNA-mRNA interaction network construction. Results: First, we investigated the key miRNAs that regulated significantly more differentially expressed genes based on GSEA method. Second, we searched for the key miRNAs based on the mRNA-miRNA interaction subnetwork involved in PTC. We identified hsa-mir-1275, hsa-mir-1291, hsa-mir-206 and hsa-mir-375 as the key miRNAs involved in PTC pathogenesis. Conclusion: The integrated analysis of the gene and miRNA expression data not only identified key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma, but also improved our understanding of the pathogenesis of PTC.

scholarly journals Placental Transcriptome Adaptations to Maternal Nutrient Restriction in Sheep

2021 ◽  
Vol 22 (14) ◽  
pp. 7654
Author(s):  
Chelsie B. Steinhauser ◽  
Colleen A. Lambo ◽  
Katharine Askelson ◽  
Gregory W. Burns ◽  
Susanta K. Behura ◽  
...  
Keyword(s):  
Mhc Class Ii ◽  
Killer Cell ◽  
National Research Council ◽  
Enrichment Analysis ◽  
Nutrient Utilization ◽  
Gene Set Enrichment Analysis ◽  
Nutrient Restriction ◽  
Placental Development ◽  
Gene Set Enrichment ◽  
Pregnant Sheep

Placental development is modified in response to maternal nutrient restriction (NR), resulting in a spectrum of fetal growth rates. Pregnant sheep carrying singleton fetuses and fed either 100% (n = 8) or 50% (NR; n = 28) of their National Research Council (NRC) recommended intake from days 35–135 of pregnancy were used to elucidate placentome transcriptome alterations at both day 70 and day 135. NR fetuses were further designated into upper (NR NonSGA; n = 7) and lower quartiles (NR SGA; n = 7) based on day 135 fetal weight. At day 70 of pregnancy, there were 22 genes dysregulated between NR SGA and 100% NRC placentomes, 27 genes between NR NonSGA and 100% NRC placentomes, and 22 genes between NR SGA and NR NonSGA placentomes. These genes mediated molecular functions such as MHC class II protein binding, signaling receptor binding, and cytokine activity. Gene set enrichment analysis (GSEA) revealed significant overrepresentation of genes for natural-killer-cell-mediated cytotoxicity in NR SGA compared to 100% NRC placentomes, and alterations in nutrient utilization pathways between NR SGA and NR NonSGA placentomes at day 70. Results identify novel factors associated with impaired function in SGA placentomes and potential for placentomes from NR NonSGA pregnancies to adapt to nutritional hardship.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

A Six-Gene Prognostic Risk Prediction Model In Hepatitis B Virus-Associated Hepatocellular Carcinoma

2021 ◽  
Vol 44 (3) ◽  
pp. E32-44
Author(s):  
Jia Shen ◽  
Ming Shu ◽  
Shujie Xie ◽  
Jia Yan ◽  
Kaile Pan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Prognostic Score ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Gene Set Enrichment ◽  
B Virus ◽  
Score Model ◽  
Normal Controls

Purpose: This study aimed to screen hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC)-related feature ribonucleic acids (RNAs) and to establish a prognostic model. Methods: The transcriptome expression data of HBV-associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus database. Differential RNAs between HBV-associated HCC and normal controls were identified by a meta-analysis of TCGA, GSE55092 and GSE121248. Weighted gene co-expression network analysis was performed to identify key RNAs and modules. A prognostic score model was established using TCGA as a training set by Cox regression analysis and was validated in E-TABM-36 dataset. Additionally, independent prognostic clinical factors were screened, and the function of lncRNAs waspredicted through Gene Set Enrichment Analysis. Results: A total of 710 consistent differential RNAs between HBV-associated HCC and normal controls were obtained, including five lncRNAs and 705 mRNAs. An optimized combination of six differential RNAs (DSCR4, DBH, ECM1, GDAP1, MATR3 and RFC4) was selected and a prognostic score model was constructed. Kaplan-Meier analysis demonstrated that the prognosis of the high-risk and low-risk groups separated by this model was significantly different in the training set and the validation set. Gene Set Enrichment Analysis showed that the co-expression genes of DSCR4 were significantly correlated with neuroactive ligand receptor interactionpathway. Conclusion: A prognostic model based on DSCR4, DBH, ECM1, GDAP1, MATR3 and RFC4 was developed that can accurately predict the prognosis of patients with HBV-associated HCC. These genes, as well as histologic grade, may serve as independent prognostic factors in HBV-associated HCC.

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