A Replication Study for Genome-Wide Gene Expression Levels in Two Layer Lines Elucidates Differentially Expressed Genes of Pathways Involved in Bone Remodeling and Immune Responsiveness

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.

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Piezophysiology of genome wide gene expression levels in the yeast Saccharomyces cerevisiae

Extremophiles ◽

10.1007/s00792-003-0322-y ◽

2003 ◽

Vol 7 (4) ◽

pp. 291-298 ◽

Cited By ~ 39

Author(s):

Hitoshi Iwahashi ◽

Hisayo Shimizu ◽

Mine Odani ◽

Yasuhiko Komatsu

Keyword(s):

Gene Expression ◽

Saccharomyces Cerevisiae ◽

Yeast Saccharomyces Cerevisiae ◽

Expression Levels ◽

Genome Wide ◽

Genome Wide Gene Expression ◽

Gene Expression Levels

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Genome-wide analysis of blood gene expression in migraine implicates immune-inflammatory pathways

Cephalalgia ◽

10.1177/0333102416686769 ◽

2017 ◽

Vol 38 (2) ◽

pp. 292-303 ◽

Cited By ~ 14

Author(s):

Zachary F Gerring ◽

Joseph E Powell ◽

Grant W Montgomery ◽

Dale R Nyholt

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Whole Blood ◽

Differentially Expressed ◽

Expression Data ◽

Biological Mechanisms ◽

Blood Gene Expression ◽

Expression Levels ◽

Inflammatory Pathways ◽

Genome Wide

Background Typical migraine is a frequent, debilitating and painful headache disorder with an estimated heritability of about 50%. Although genome-wide association (GWA) studies have identified over 40 single nucleotide polymorphisms associated with migraine, further research is required to determine their biological role in migraine susceptibility. Therefore, we performed a study of genome-wide gene expression in a large sample of 83 migraine cases and 83 non-migraine controls to determine whether altered expression levels of genes and pathways could provide insights into the biological mechanisms underlying migraine. Methods We assessed whole blood gene expression data for 17994 expression probes measured using IlluminaHT-12 v4.0 BeadChips. Differential expression was assessed using multivariable logistic regression. Gene expression probes with a nominal p value < 0.05 were classified as differentially expressed. We identified modules of co-regulated genes and tested them for enrichment of differentially expressed genes and functional pathways using a false discovery rate <0.05. Results Association analyses between migraine and probe expression levels, adjusted for age and gender, revealed an excess of small p values, but there was no significant single-probe association after correction for multiple testing. Network analysis of pooled expression data identified 10 modules of co-expressed genes. One module harboured a significant number of differentially expressed genes and was strongly enriched with immune-inflammatory pathways, including multiple pathways expressed in microglial cells. Conclusions These data suggest immune-inflammatory pathways play an important role in the pathogenesis, manifestation, and/or progression of migraine in some patients. Furthermore, gene-expression associations are measurable in whole blood, suggesting the analysis of blood gene expression can inform our understanding of the biological mechanisms underlying migraine, identify biomarkers, and facilitate the discovery of novel pathways and thus determine new targets for drug therapy.

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

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1157 ◽

2005 ◽

Vol 4 (1) ◽

Cited By ~ 23

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.

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

Human Genomics ◽

10.1186/s40246-021-00308-5 ◽

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.

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Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽

10.3390/agronomy11010092 ◽

2021 ◽

Vol 11 (1) ◽

pp. 92

Author(s):

Joon Seon Lee ◽

Lexuan Gao ◽

Laura Melissa Guzman ◽

Loren H. Rieseberg

Keyword(s):

Gene Expression ◽

Alternative Splicing ◽

Mixed Model ◽

Agricultural Land ◽

Alcohol Fermentation ◽

Expression Levels ◽

Flooding Stress ◽

Genome Wide ◽

And Control ◽

Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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Intra- and inter-specific variations of gene expression levels in yeast are largely neutral

10.1101/089995 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jian-Rong Yang ◽

Calum Maclean ◽

Chungoo Park ◽

Huabin Zhao ◽

Jianzhi Zhang

Keyword(s):

Gene Expression ◽

Genome Sequence ◽

Large Fraction ◽

General Role ◽

Expression Levels ◽

Genome Wide ◽

Sequence Variations ◽

Expression Evolution ◽

Molecular Phenotypes ◽

Gene Expression Levels

ABSTRACTIt is commonly, although not universally, accepted that most intra- and inter-specific genome sequence variations are more or less neutral, whereas a large fraction of organism-level phenotypic variations are adaptive. Gene expression levels are molecular phenotypes that bridge the gap between genotypes and corresponding organism-level phenotypes. Yet, it is unknown whether natural variations in gene expression levels are mostly neutral or adaptive. Here we address this fundamental question by genome-wide profiling and comparison of gene expression levels in nine yeast strains belonging to three closely related Saccharomyces species and originating from five different ecological environments. We find that the transcriptome-based clustering of the nine strains approximates the genome sequence-based phylogeny irrespective of their ecological environments. Remarkably, only ∼0.5% of genes exhibit similar expression levels among strains from a common ecological environment, no greater than that among strains with comparable phylogenetic relationships but different environments. These and other observations strongly suggest that most intra- and inter-specific variations in yeast gene expression levels result from the accumulation of random mutations rather than environmental adaptations. This finding has profound implications for understanding the driving force of gene expression evolution, genetic basis of phenotypic adaptation, and general role of stochasticity in evolution.

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

10.21203/rs.3.rs-19489/v1 ◽

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.

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