CD8+ T cell gene expression analysis identifies differentially expressed genes between multiple sclerosis patients and healthy controls

Background Genetic and clinical observations have indicated T cells are involved in MS pathology. There is little insight in how T cells are involved and whether or not these can be used as markers for MS. Objectives Analysis of the gene expression profiles of circulating CD8+ T cells of MS patients compared to healthy controls. Methods RNA from purified CD8+ T cells was sequenced and analyzed for differential gene expression. Pathway analyses of genes at several p-value cutoffs were performed to identify putative pathways involved. Results We identified 36 genes with significant differential gene expression in MS patients. Four genes reached at least 2-fold differences in expression. The majority of differentially expressed genes was higher expressed in MS patients. Genes associated to MS in GWAS showed enrichment amongst the differentially expressed genes. We did not identify enrichment of specific pathways amongst the differentially expressed genes in MS patients. Conclusions CD8+ T cells of MS patients show differential gene expression, with predominantly higher activity of genes in MS patients. We do not identify specific biological pathways in our study. More detailed analysis of CD8+ T cells and subtypes of these may increase understanding of how T cells are involved in MS.

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No differential gene expression for CD4+ T cells of MS patients and healthy controls

Multiple Sclerosis Journal - Experimental Translational and Clinical ◽

10.1177/2055217319856903 ◽

2019 ◽

Vol 5 (2) ◽

pp. 205521731985690 ◽

Cited By ~ 1

Author(s):

Ina S Brorson ◽

Anna Eriksson ◽

Ingvild S Leikfoss ◽

Elisabeth G Celius ◽

Pål Berg-Hansen ◽

...

Keyword(s):

Gene Expression ◽

Multiple Sclerosis ◽

T Cells ◽

Differential Gene Expression ◽

Genetic Variants ◽

Healthy Controls ◽

Expression Studies ◽

Differential Gene ◽

Gene Expression Studies ◽

Multiple Sclerosis Patients

Background Multiple sclerosis-associated genetic variants indicate that the adaptive immune system plays an important role in the risk of developing multiple sclerosis. It is currently not well understood how these multiple sclerosis-associated genetic variants contribute to multiple sclerosis risk. CD4+ T cells are suggested to be involved in multiple sclerosis disease processes. Objective We aim to identify CD4+ T cell differential gene expression between multiple sclerosis patients and healthy controls in order to understand better the role of these cells in multiple sclerosis. Methods We applied RNA sequencing on CD4+ T cells from multiple sclerosis patients and healthy controls. Results We did not identify significantly differentially expressed genes in CD4+ T cells from multiple sclerosis patients. Furthermore, pathway analyses did not identify enrichment for specific pathways in multiple sclerosis. When we investigated genes near multiple sclerosis-associated genetic variants, we did not observe significant enrichment of differentially expressed genes. Conclusion We conclude that CD4+ T cells from multiple sclerosis patients do not show significant differential gene expression. Therefore, gene expression studies of all circulating CD4+ T cells may not result in viable biomarkers. Gene expression studies of more specific subsets of CD4+ T cells remain justified to understand better which CD4+ T cell subsets contribute to multiple sclerosis pathology.

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HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽

10.3390/v13020244 ◽

2021 ◽

Vol 13 (2) ◽

pp. 244 ◽

Cited By ~ 1

Author(s):

Antonio Victor Campos Coelho ◽

Rossella Gratton ◽

João Paulo Britto de Melo ◽

José Leandro Andrade-Santos ◽

Rafael Lima Guimarães ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Differentially Expressed Genes ◽

Cd4 T Cells ◽

Expression Profiles ◽

Meta Analysis ◽

Differentially Expressed ◽

Rna Seq ◽

Infected Cells ◽

Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

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PADGE: analysis of heterogeneous patterns of differential gene expression

Physiological Genomics ◽

10.1152/physiolgenomics.00259.2006 ◽

2007 ◽

Vol 32 (1) ◽

pp. 154-159 ◽

Cited By ~ 15

Author(s):

Li Li ◽

Amitabha Chaudhuri ◽

John Chant ◽

Zhijun Tang

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Web Application ◽

Statistical Power ◽

Profile Analysis ◽

Expression Profiles ◽

Differentially Expressed ◽

Cancer Genes ◽

Data Set ◽

Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

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Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression

Blood ◽

10.1182/blood-2009-08-236422 ◽

2009 ◽

Vol 114 (23) ◽

pp. 4847-4858 ◽

Cited By ~ 53

Author(s):

Kunju Sridhar ◽

Douglas T. Ross ◽

Robert Tibshirani ◽

Atul J. Butte ◽

Peter L. Greenberg

Keyword(s):

Gene Expression ◽

Acute Myeloid Leukemia ◽

Myelodysplastic Syndrome ◽

Differential Gene Expression ◽

Myeloid Leukemia ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Differentially Expressed ◽

Differential Gene ◽

Acute Myeloid

AbstractMicroarray analysis with 40 000 cDNA gene chip arrays determined differential gene expression profiles (GEPs) in CD34+ marrow cells from myelodysplastic syndrome (MDS) patients compared with healthy persons. Using focused bioinformatics analyses, we found 1175 genes significantly differentially expressed by MDS versus normal, requiring a minimum of 39 genes to separately classify these patients. Major GEP differences were demonstrated between healthy and MDS patients and between several MDS subgroups: (1) those whose disease remained stable and those who subsequently transformed (tMDS) to acute myeloid leukemia; (2) between del(5q) and other MDS patients. A 6-gene “poor risk” signature was defined, which was associated with acute myeloid leukemia transformation and provided additive prognostic information for International Prognostic Scoring System Intermediate-1 patients. Overexpression of genes generating ribosomal proteins and for other signaling pathways was demonstrated in the tMDS patients. Comparison of del(5q) with the remaining MDS patients showed 1924 differentially expressed genes, with underexpression of 1014 genes, 11 of which were within the 5q31-32 commonly deleted region. These data demonstrated (1) GEPs distinguishing MDS patients from healthy and between those with differing clinical outcomes (tMDS vs those whose disease remained stable) and cytogenetics [eg, del(5q)]; and (2) molecular criteria refining prognostic categorization and associated biologic processes in MDS.

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СONSECUTIVE INTEGRATION OF AVAILABLE MICROARRAY DATA FOR ANALYSIS OF DIFFERENTIAL GENE EXPRESSION IN HUMAN PLACENTA

Biotechnologia Acta ◽

10.15407/biotech14.01.38 ◽

2021 ◽

Vol 14 (1) ◽

pp. 38-45

Author(s):

O. Lykhenko ◽

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Differentially Expressed Genes ◽

Human Placenta ◽

Normal Pregnancy ◽

Integrative Analysis ◽

Differentially Expressed ◽

Second Trimester ◽

Placental Development ◽

Differential Gene

The purpose of the study was to provide the pipeline for processing of publicly available unprocessed data on gene expression via integration and differential gene expression analysis. Data collection from open gene expression databases, normalization and integration into a single expression matrix in accordance with metadata and determination of differentially expressed genes were fulfilled. To demonstrate all stages of data processing and integrative analysis, there were used the data from gene expression in the human placenta from the first and second trimesters of normal pregnancy. The source code for the integrative analysis was written in the R programming language and publicly available as a repository on GitHub. Four clusters of functionally enriched differentially expressed genes were identified for the human placenta in the interval between the first and second trimester of pregnancy. Immune processes, developmental processes, vasculogenesis and angiogenesis, signaling and the processes associated with zinc ions varied in the considered interval between the first and second trimester of placental development. The proposed sequence of actions for integrative analysis could be applied to any data obtained by microarray technology.

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Identification of gene expression alterations in C. elegans adr-2 mutants using high- throughput sequencing

Proceedings of IMPRS ◽

10.18060/23616 ◽

2019 ◽

Vol 2 (1) ◽

Author(s):

Jackson Townsend ◽

Heather A. Hundley

Keyword(s):

Gene Expression ◽

Young Adult ◽

Rna Editing ◽

Differential Gene Expression ◽

Differentially Expressed Genes ◽

High Throughput Sequencing ◽

Life Stage ◽

Differentially Expressed ◽

C Elegans ◽

Differential Gene

Background and Hypothesis: RNA editing is one of several mechanisms regulating gene expression. One type of RNA editing, the deamination of adenosine to inosine, is carried out by ADAR enzymes. ADAR enzymes are essential for neural function and aberrant editing is implicated in various forms of neuropathology. C. elegans lacking the RNA editing enzyme, ADR-2, are viable allowing us to ascertain how loss of RNA editing affects neural gene expression. The effects of loss of adr-2 on neural gene expression will be analyzed in both the first larval (L1) and young adult stages. We hypothesize that the transcriptome will change depending on life stage and the presence of ADR-2. Methods: Three replicates of neural cells isolated from wild type and adr-2(-) L1 and young adult stage animals were obtained. Total RNA was extracted from each population and mRNA was isolated using an oligo-dT bead. The mRNA was fragmented, and reverse transcribed to generate a complentary DNA (cDNA) library. The cDNA was sequenced by a facility at Indiana University. Quality of the library was evaluated using FASTqc. DE-seq2 software evaluated the differential gene expression. Results: I examined differential gene expression in two life stages of the WT and adr-2 neural samples. After obtaining the differentially expressed genes, the portions of the transcriptome that require ADR-2 was determined. WT young adults showed increased (3715) and decreased (2504) expression of neural genes when compared to the L1 stage. Many differentially expressed genes required adr-2 (~40% of the upregulated and 78% of the downregulated genes.) In addition, some genes were uniquely altered (631 upregulated, 196 downregulated) in the absence of adr-2. Conclusion and Potential Impact: The life stage and presence of ADR-2 alter the neural transcriptome and this function changes throughout development. Future studies will determine whether these genes are altered due to the lack of RNA editing or binding by ADR-2.

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Global Characterization of Differential Gene Expression Profiles in Mouse Vγ1+ and Vγ4+ γδ T Cells

PLoS ONE ◽

10.1371/journal.pone.0112964 ◽

2014 ◽

Vol 9 (11) ◽

pp. e112964 ◽

Cited By ~ 6

Author(s):

Peng Dong ◽

Siya Zhang ◽

Menghua Cai ◽

Ning Kang ◽

Yu Hu ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Differential Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Γδ T Cells ◽

Differential Gene

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A Distinct Expression of Various Gene Subsets in CD34+ Cells Obtained From Patients with Early and Advanced Myelodysplastic Syndrome.

Blood ◽

10.1182/blood.v114.22.3795.3795 ◽

2009 ◽

Vol 114 (22) ◽

pp. 3795-3795

Author(s):

Monika Belickova ◽

Jaroslav Cermak ◽

Alzbeta Vasikova ◽

Eva Budinska

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Differentially Expressed Genes ◽

Conflicts Of Interest ◽

Expression Profiles ◽

Differentially Expressed ◽

Abnormal Development ◽

Statistical Hypothesis ◽

P Value ◽

Cd34 Cells

Abstract Abstract 3795 Poster Board III-731 Gene expression profiles of CD34+ cells were compared between a cohort of 51 patients with MDS or AML from MDS and 7 healthy controls. The patients were classified according to the WHO criteria as follows: 5q- syndrome (n=7), RA (n=3), RARS (n=2), RCMD (n=10), RAEB-1 (n=7), RAEB-2 (n=15), and AML with MLD (multilineage dysplasia) (n=7). HumanRef-8 v2 Expression Bead Chips (Illumina) were used to generate expression profiles of the samples for >22,000 transcripts. The raw data were normalized data with the R software, lumi package. Normalized data were filtered by detection p-value <0.01, resulting in total number of 9811genes. To identify differentially expressed genes we performed two parallel statistical hypothesis testings: Analysis of Variance (ANOVA) together with Tukey test and empirical bayesian thresholding correction for multiple testing problem; and Significance Analysis of Microarrays (SAM). The results were confirmed by real-time quantitative PCR for six genes (TaqMan Gene Expression Assays). Hierarchical clustering of significantly differentially expressed genes clearly separated patients and controls, 5q-syndrome and RAEB-1 as a separate entities confirming usefulness of WHO classification subgroups. The most up-regulated genes in all patients included HBG2, HBG1, CYBRD1, HSPA1B, ANGPT1, and MYC. We assume that expression changes in globin genes, both fetal and adult globins (HBG2, HBG1 and HBA1, HBB) may play role not only in dysregulation of erythropoiesis but also in the disease progression or leukemic transformation of MDS. Among the most down-regulated genes, 13 genes related to B-lymphopoiesis (e.g. POU2AF1, VPREB1, VPREB3, CD79A, EBF1, LEF1, BCL3, IRF8 & IRF4) were detected, suggesting the abnormal development of B-cell progenitors in all MDS patients. Some of these genes (e.g. VPREB3, LEF1) showed decreasing trend in expression level from early to advanced MDS with the lowest expression in AML with MLD. Patients with advanced MDS had significantly decreased expression of genes involved in in the mitotic cell cycle, DNA replication, and chromosome segregation compared to early MDS where these gene subsets were up-regulated. The DAVID database also identified de-regulation in the cell cycle pathway through its 7 genes (CDC25C, CDC7, CDC20, ORC1L, CCNB2, BUB1, & CCNA2). On the other hand, advanced MDS patients showed significant up-regulation of proto-oncogenes (BMI1, MERTK) and genes related to angiogenesis (ANGPT1), anti-apoptosis (VNN1). The results confirm on molecular basis that increased cell proliferation and resistance to apoptosis together with a loss of cell cycle control, damaged DNA repair and altered immune response may play an important role in the expansion of malignant clone in MDS patients. The study was supported by Grant NR-9235 obtained from the Ministry of Health, Czech Republic. Disclosures: No relevant conflicts of interest to declare.

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ViDGER: An R package for integrative interpretation of differential gene expression results of RNA-seq data

10.1101/268896 ◽

2018 ◽

Cited By ~ 4

Author(s):

Adam McDermaid ◽

Brandon Monier ◽

Jing Zhao ◽

Qin Ma

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Differential Gene Expression ◽

Differentially Expressed Genes ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Differential Gene

AbstractDifferential gene expression (DGE) is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we present an R package, ViDGER (Visualization of Differential Gene Expression Results using R), which contains nine functions that generate information-rich visualizations for the interpretation of DGE results from three widely-used tools, Cuffdiff, DESeq2, and edgeR.

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Alternative splicing and gene expression play contrasting roles in the parallel phenotypic evolution of a salmonid fish

10.1101/2020.05.11.087973 ◽

2020 ◽

Author(s):

Arne Jacobs ◽

Kathryn R. Elmer

Keyword(s):

Gene Expression ◽

Alternative Splicing ◽

Differential Gene Expression ◽

Differentially Expressed Genes ◽

Adaptive Evolution ◽

Salmonid Fish ◽

Differentially Expressed ◽

Molecular Processes ◽

Regulatory Regions ◽

Differential Gene

AbstractUnderstanding the contribution of different molecular processes to the evolution and development of divergent phenotypes is crucial for identifying the molecular routes of rapid adaptation. Here, we used RNA-seq data to compare patterns of alternative splicing and differential gene expression in a case of parallel adaptive evolution, the replicated postglacial divergence of the salmonid fish Arctic charr (Salvelinus alpinus) into benthic and pelagic ecotypes across multiple independent lakes.We found that genes that were differentially spliced and differentially expressed between the benthic and pelagic ecotypes were mostly independent (<6% overlap) and were involved in different processes. Differentially spliced genes were primarily enriched for muscle development and functioning, while differentially expressed genes were mostly involved in energy metabolism, immunity and growth. Together, these likely explain different axes of divergence between ecotypes in swimming performance and activity. Furthermore, we found that alternative splicing and gene expression are mostly controlled by independent cis-regulatory quantitative trait loci (<3.4% overlap). Cis-regulatory regions were associated with the parallel divergence in splicing (16.5% of intron clusters) and expression (6.7 - 10.1% of differentially expressed genes), indicating shared regulatory variation across ecotype pairs. Contrary to theoretical expectation, we found that differentially spliced genes tended to be highly central in regulatory networks (‘hub genes’) and were annotated to significantly more gene ontology terms compared to non-differentially spliced genes, consistent with a higher level of connectivity and pleiotropy.Together, our results suggest that the concerted regulation of alternative splicing and differential gene expression through different regulatory regions leads to the divergence of complementary phenotypes important for local adaptation. This study provides novel insights into the importance of contrasting but putatively complementary molecular processes for rapid and parallel adaptive evolution.

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