scholarly journals Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

2017 ◽  
Author(s):  
John D. Blischak ◽  
Ludovic Tailleux ◽  
Marsha Myrthil ◽  
Cécile Charlois ◽  
Emmanuel Bergot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Healthy Individuals ◽  
Data Set ◽  
Expression Levels ◽  
Infected Cells ◽  
Latent Tb ◽  
Gene Expression Levels

ABSTRACTTuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the world’s population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.

Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Xing Qiu ◽  
Lev Klebanov ◽  
Andrei Yakovlev
Keyword(s):  
Gene Expression ◽  
Statistical Inference ◽  
Differentially Expressed Genes ◽  
Empirical Bayes ◽  
Biological Data ◽  
Differentially Expressed ◽  
Test Statistics ◽  
Gene Expressions ◽  
Expression Levels ◽  
Gene Expression Levels

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely related methods employing a two-component mixture model, represent typical examples. It is frequently assumed that dependence between gene expressions (or associated test statistics) is sufficiently weak to justify the application of such methods for selecting differentially expressed genes. By applying resampling techniques to simulated and real biological data sets, we have studied a potential impact of the correlation between gene expression levels on the statistical inference based on the empirical Bayes methodology. We report evidence from these analyses that this impact may be quite strong, leading to a high variance of the number of differentially expressed genes. This study also pinpoints specific components of the empirical Bayes method where the reported effect manifests itself.

scholarly journals Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rowan AlEjielat ◽  
Anas Khaleel ◽  
Amneh H. Tarkhan
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Differentially Expressed Genes ◽  
Gene Network ◽  
Disease Diagnosis ◽  
Receptor Activation ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Inflammatory Disorder ◽  
Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

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

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
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.

scholarly journals HIV-Associated Neurocognitive Disorder (HAND) Biomarker Identification: Significance Analysis of Microarrays and Two Persuasive Approaches with Random Forest

2020 ◽  
Author(s):  
Hansapani Rodrigo ◽  
Bryan Martinez ◽  
Roberto De La Garza ◽  
Upal Roy
Keyword(s):  
Gene Expression ◽  
White Matter ◽  
Basal Ganglia ◽  
Frontal Cortex ◽  
Differentially Expressed ◽  
Control Group ◽  
Expression Levels ◽  
Significance Analysis ◽  
The Subject ◽  
Gene Expression Levels

Abstract Background: HIV Associated Neurological Disorders (HAND) is relatively common among people with HIV-1 infection, even those taking combined antiretroviral treatment (cART). Genome-wide screening of transcription regulation in brain tissue helps in identifying substantial abnormalities present in patients’ gene transcripts and to discover possible biomarkers for HAND. This study explores the possibility of identifying differentially expressed (DE) genes, which can serve as potential biomarkers to detect HAND. In this study, we have investigated the gene expression levels of three subject groups with different impairment levels of HAND along with a control group in three distinct brain sectors: white matter, frontal cortex, and basal ganglia. Methods: Linear models with weighted least squares along with Benjamini-Hochberg multiple corrections were used to identify DE genes in each brain region. Genes with an adjusted p-value of less than 0.01 were identified as differentially expressed. Principal component analyses (PCA) were performed to detect any groupings among the subject groups. Significance Analysis of Microarrays (SAM) and random forests (RF) methods with two distinct approaches were used to identify DE genes. Results: A total of 710 genes in basal ganglia, 794 genes in the frontal cortex, and 1481 genes in white matter were screened. The highest proportion of DE genes was observed within the two brain regions, frontal neocortex, and basal ganglia. PCA analyses do not exhibit clear groupings among four subject groups. SAM and RF models reveal the genes, CIRBP, RBM3, GPNMB, ISG15, IFIT6, IFI6, and IFIT3, to have DE genes in the frontal cortex or basal ganglia among the subject groups. The gene, GADD45A, a protein-coding gene whose transcript levels tend to increase with stressful growth arrest conditions, was consistently ranked among the top genes by both RF models within the frontal cortex. Conclusions: Our study contributes to a comprehensive understanding of the gene expression levels of the subject with different severity levels of HAND. Several genes that appear to play critical roles in the inflammatory response have been found, and they have an excellent potential to be used as biomarkers to detect HAND under further investigations.

scholarly journals Transcriptomic Analysis Suggests Genes Expressed Stage-Independently and Stage–Dependently Modulating the Wing Dimorphism of the Brown Planthopper

Genes ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 19 ◽  
Author(s):  
Chao Zhang ◽  
Xiang-Dong Liu
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Brown Planthopper ◽  
Adherens Junction ◽  
Sugar Metabolism ◽  
Differentially Expressed ◽  
Wing Dimorphism ◽  
Expression Levels ◽  
Three Stages ◽  
Gene Expression Levels

Wing dimorphism is considered as an adaptive trait of insects. Brown planthoppers (BPHs) Nilaparvata lugens, a serious pest of rice, are either macropterous or brachypterous. Genetic and environmental factors are both likely to control wing morph determination in BPHs, but the hereditary law and genes network are still unknown. Here, we investigated changes in gene expression levels between macropterous and brachypterous BPHs by creating artificially bred morphotype lines. The nearly pure-bred strains of macropterous and brachypterous BPHs were established, and their transcriptomes and gene expression levels were compared. Over ten-thousand differentially expressed genes (DEGs) between macropterous and brachypterous strains were found in the egg, nymph, and adult stages, and the three stages shared 6523 DEGs. The regulation of actin cytoskeleton, focal adhesion, tight junction, and adherens junction pathways were consistently enriched with DEGs across the three stages, whereas insulin signaling pathway, metabolic pathways, vascular smooth muscle contraction, platelet activation, oxytocin signaling pathway, sugar metabolism, and glycolysis/gluconeogenesis were significantly enriched by DEGs in a specific stage. Gene expression trend profiles across three stages were different between the two strains. Eggs, nymphs, and adults from the macropterous strain were distinguishable from the brachypterous based on gene expression levels, and genes that were related to wing morphs were differentially expressed between wing strains or strain × stage. A proposed mode based on genes and environments to modulate the wing dimorphism of BPHs was provided.

scholarly journals Transcriptome and Small RNA Combined Sequencing Analysis of Cold Tolerance in Non-heading Chinese Cabbage

2021 ◽  
Vol 12 ◽  
Author(s):  
Jin Wang ◽  
Qinxue Zhang ◽  
Xiong You ◽  
Xilin Hou
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Small Rna ◽  
Target Genes ◽  
Differentially Expressed ◽  
Sequencing Analysis ◽  
Pak Choi ◽  
Expression Levels ◽  
Heading Chinese Cabbage ◽  
Gene Expression Levels

BackgroundNon-heading Chinese cabbage (Brassica rapa ssp. chinensis) is an important leaf vegetable grown worldwide. However, there has currently been not enough transcriptome and small RNA combined sequencing analysis of cold tolerance, which hinders further functional genomics research.ResultsIn this study, 63.43 Gb of clean data was obtained from the transcriptome analysis. The clean data of each sample reached 6.99 Gb, and the basic percentage of Q30 was 93.68% and above. The clean reads of each sample were sequence aligned with the designated reference genome (Brassica rapa, IVFCAASv1), and the efficiency of the alignment varied from 81.54 to 87.24%. According to the comparison results, 1,860 new genes were discovered in Pak-choi, of which 1,613 were functionally annotated. Among them, 13 common differentially expressed genes were detected in all materials, including seven upregulated and six downregulated. At the same time, we used quantitative real-time PCR to confirm the changes of these gene expression levels. In addition, we sequenced miRNA of the same material. Our findings revealed a total of 34,182,333 small RNA reads, 88,604,604 kinds of small RNAs, among which the most common size was 24 nt. In all materials, the number of common differential miRNAs is eight. According to the corresponding relationship between miRNA and its target genes, we carried out Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis on the set of target genes on each group of differentially expressed miRNAs. Through the analysis, it is found that the distributions of candidate target genes in different materials are different. We not only used transcriptome sequencing and small RNA sequencing but also used experiments to prove the expression levels of differentially expressed genes that were obtained by sequencing. Sequencing combined with experiments proved the mechanism of some differential gene expression levels after low-temperature treatment.ConclusionIn all, this study provides a resource for genetic and genomic research under abiotic stress in Pak-choi.

scholarly journals The Genetic Architecture of Gene Expression Levels in Wild Baboons

2014 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Nonhuman Primates ◽  
Rna Seq ◽  
Gene Expression Variation ◽  
Data Set ◽  
Expression Levels ◽  
Expression Variation ◽  
Wild Baboons ◽  
Gene Expression Levels

Gene expression variation is well documented in human populations and its genetic architecture has been extensively explored. However, we still know little about the genetic architecture of gene expression variation in other species, particularly our closest living relatives, the nonhuman primates. To address this gap, we performed an RNA sequencing (RNA-seq)-based study of 63 wild baboons, members of the intensively studied Amboseli baboon population in Kenya. Our study design allowed us to measure gene expression levels and identify genetic variants using the same data set, enabling us to perform complementary mapping of putative cis-acting expression quantitative trait loci (eQTL) and measurements of allele-specific expression (ASE) levels. We discovered substantial evidence for genetic effects on gene expression levels in this population. Surprisingly, we found more power to detect individual eQTL in the baboons relative to a HapMap human data set of comparable size, probably as a result of greater genetic variation, enrichment of SNPs with high minor allele frequencies, and longer-range linkage disequilibrium in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes. Interestingly, genes with eQTL significantly overlapped between the baboon and human data sets, suggesting that some genes may tolerate more genetic perturbation than others, and that this property may be conserved across species. Finally, we used a Bayesian sparse linear mixed model to partition genetic, demographic, and early environmental contributions to variation in gene expression levels. We found a strong genetic contribution to gene expression levels for almost all genes, while individual demographic and environmental effects tended to be more modest. Together, our results establish the feasibility of eQTL mapping using RNA-seq data alone, and act as an important first step towards understanding the genetic architecture of gene expression variation in nonhuman primates.

scholarly journals The genetic architecture of gene expression levels in wild baboons

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Jenny Tung ◽  
Xiang Zhou ◽  
Susan C Alberts ◽  
Matthew Stephens ◽  
Yoav Gilad
Keyword(s):  
Gene Expression ◽  
Genetic Architecture ◽  
Eqtl Mapping ◽  
Rna Seq ◽  
Specific Expression ◽  
Data Set ◽  
Expression Levels ◽  
Trait Locus ◽  
Eqtl Data ◽  
Gene Expression Levels

Primate evolution has been argued to result, in part, from changes in how genes are regulated. However, we still know little about gene regulation in natural primate populations. We conducted an RNA sequencing (RNA-seq)-based study of baboons from an intensively studied wild population. We performed complementary expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses, discovering substantial evidence for, and surprising power to detect, genetic effects on gene expression levels in the baboons. eQTL were most likely to be identified for lineage-specific, rapidly evolving genes; interestingly, genes with eQTL significantly overlapped between baboons and a comparable human eQTL data set. Our results suggest that genes vary in their tolerance of genetic perturbation, and that this property may be conserved across species. Further, they establish the feasibility of eQTL mapping using RNA-seq data alone, and represent an important step towards understanding the genetic architecture of gene expression in primates.

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