Differential Gene Expression Methods Research Articles

Identification of Predictor Genes for Feed Efficiency in Beef Cattle by Applying Machine Learning Methods to Multi-Tissue Transcriptome Data.

Machine learning (ML) methods have shown promising results in identifying genes when applied to large transcriptome datasets. However, no attempt has been made to compare the performance of combining different ML methods together in the prediction of high feed efficiency (HFE) and low feed efficiency (LFE) animals. In this study, using RNA sequencing data of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle, and pituitary) from nine HFE and nine LFE Nellore bulls, we evaluated the prediction accuracies of five analytical methods in classifying FE animals. These included two conventional methods for differential gene expression (DGE) analysis (t-test and edgeR) as benchmarks, and three ML methods: Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and combination of both RF and XGBoost (RX). Utility of a subset of candidate genes selected from each method for classification of FE animals was assessed by support vector machine (SVM). Among all methods, the smallest subsets of genes (117) identified by RX outperformed those chosen by t-test, edgeR, RF, or XGBoost in classification accuracy of animals. Gene co-expression network analysis confirmed the interactivity existing among these genes and their relevance within the network related to their prediction ranking based on ML. The results demonstrate a great potential for applying a combination of ML methods to large transcriptome datasets to identify biologically important genes for accurately classifying FE animals.

GEOlimma: differential expression analysis and feature selection using pre-existing microarray data

BackgroundDifferential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.ResultsIn this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.ConclusionsOur results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles.

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

Genome Regulation and Gene Interaction Networks Inferred From Muscle Transcriptome Underlying Feed Efficiency in Pigs

Improvement of feed efficiency (FE) is key for Sustainability and cost reduction in pig production. Our aim was to characterize the muscle transcriptomic profiles in Danbred Duroc (Duroc; n = 13) and Danbred Landrace (Landrace; n = 28), in relation to FE for identifying potential biomarkers. RNA-seq data on the 41 pigs was analyzed employing differential gene expression methods, gene-gene interaction and network analysis, including pathway and functional analysis. We also compared the results with genome regulation in human exercise data, hypothesizing that increased FE mimics processes triggered in exercised muscle. In the differential expression analysis, 13 genes were differentially expressed, including: MRPS11, MTRF1, TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, the divergent count, we performed pathway enrichment analysis. We found five significantly enriched pathways related to feed conversion ratio (FCR). These pathways were mainly related to mitochondria, and summarized in the mitochondrial translation elongation (MTR) pathway. In the gene interaction analysis, the most interesting genes included the mitochondrial genes: PPIF, MRPL35, NDUFS4 and the fat metabolism and obesity genes: AACS, SMPDL3B, CTNNBL1, NDUFS4, and LIMD2. In the network analysis, we identified two modules significantly correlated with FCR. Pathway enrichment of module genes identified MTR, electron transport chain and DNA repair as enriched pathways. The network analysis revealed the mitochondrial gene group NDUF as key network hub genes, showing their potential as biomarkers. Results show that genes related to human exercise were enriched in identified FCR related genes. We conclude that mitochondrial activity is a key driver for FCR in muscle tissue, and mitochondrial genes could be potential biomarkers for FCR in pigs.

Modeling differentially expressed genes in patient tumors to guide expression-based biomarker development.

3627 Background: Differential gene expression (DGE) methods, initially developed for analyzing bulk RNA changes in pure tumor cell lines under experimental settings, are commonly used to identify biomarkers in and infer biological differences between patient tumor samples, which are admixtures of tumor and non-tumor components. Methods to sensitively and accurately detect cell type-specific expression differences in admixed patient samples are not well characterized but may greatly affect emerging targeted and immunotherapy biomarker strategies. To address this issue, we developed a simulation framework to benchmark our ability to detect changes in tumor-intrinsic gene expression. Methods: Pseudobulk RNAseq melanoma cohorts were simulated by sampling from melanoma single cell RNAseq data. Simulation parameters were optimized to maximize concordance of gene expression means and variances (Spearman r = 0.81, 0.68, respectively) between the TCGA SKCM cohort (n = 462) and matched simulated cohort, and then validated in two independent melanoma cohorts (n = 42, 129; means Spearman r = 0.80, 0.78; variances Spearman r = 0.68, 0.63). Using this simulation framework, we benchmarked the effect of sample size, magnitude of differential expression, and differences in cell type proportions on the sensitivity and positive predictive value (PPV) of detecting true differentially expressed genes in the tumor-intrinsic compartment. Results: Reference cohorts of 50 total tumors (n = 10) were simulated to contain a 2 standard deviation tumor-intrinsic expression change in 50 randomly selected genes and a 11% difference in mean purity between two equally sized 25-tumor subgroups. DGE analysis using DESeq2 with an FDR q-value threshold of 0.1 yielded a sensitivity of 0.37 and PPV of 0.29. DGE analysis of the same simulated cohorts using a non-parametric Mann-Whitney U test with an FDR q-value threshold of 0.1 yielded a sensitivity of 0.13 and PPV of 0.76. Conclusions: Commonly used DGE methods for existing expression-based biomarker strategies have poor sensitivity and PPV in admixed tumor samples, limiting our ability to find meaningful transcriptional biomarkers in clinical cohorts. We are currently developing methods to more accurately detect true differentially expressed genes in admixed bulk RNAseq samples and applying these approaches for biomarker discovery in immunotherapy-treated patient cohorts and other clinical tumor cohorts.

Quantitative Trait Locus and Brain Expression of HLA-DPA1 Offers Evidence of Shared Immune Alterations in Psychiatric Disorders.

Genome-wide association studies of schizophrenia encompassing the major histocompatibility locus (MHC) were highly significant following genome-wide correction. This broad region implicates many genes including the MHC complex class II. Within this interval we examined the expression of two MHC II genes (HLA-DPA1 and HLA-DRB1) in brain from individual subjects with schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and controls by differential gene expression methods. A third MHC II mRNA, CD74, was studied outside of the MHC II locus, as it interacts within the same immune complex. Exon microarrays were performed in anterior cingulate cortex (ACC) in BD compared to controls, and both HLA-DPA1 and CD74 were decreased in expression in BD. The expression of HLA-DPA1 and CD74 were both reduced in hippocampus, amygdala, and dorsolateral prefrontal cortex regions in SZ and BD compared to controls by specific qPCR assay. We found several novel HLA-DPA1 mRNA variants spanning HLA-DPA1 exons 2-3-4 as suggested by exon microarrays. The intronic rs9277341 SNP was a significant cis expression quantitative trait locus (eQTL) that was associated with the total expression of HLA-DPA1 in five brain regions. A biomarker study of MHC II mRNAs was conducted in SZ, BD, MDD, and control lymphoblastic cell lines (LCL) by qPCR assay of 87 subjects. There was significantly decreased expression of HLA-DPA1 and CD74 in BD, and trends for reductions in SZ in LCLs. The discovery of multiple splicing variants in brain for HLA-DPA1 is important as the HLA-DPA1 gene is highly conserved, there are no reported splicing variants, and the functions in brain are unknown. Future work on the function and localization of MHC Class II proteins in brain will help to understand the role of alterations in neuropsychiatric disorders. The HLA-DPA1 eQTL is located within a large linkage disequilibrium block that has an irrefutable association with schizophrenia. Future tests in a larger cohort are needed to determine the significance of this eQTL association with schizophrenia. Our findings support the long-held hypothesis that alterations in immune function are associated with the pathophysiology of psychiatric disorders.

Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data.

BackgroundThe advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.ResultsWe present a discrete, distributional method for differential gene expression (D3E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D3E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D3E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D3E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.ConclusionEvaluation using synthetic data shows that D3E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D3E makes it possible to gain additional biological insights.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Molecular Networks of Human Muscle Adaptation to Exercise and Age

Physical activity and molecular ageing presumably interact to precipitate musculoskeletal decline in humans with age. Herein, we have delineated molecular networks for these two major components of sarcopenic risk using multiple independent clinical cohorts. We generated genome-wide transcript profiles from individuals (n = 44) who then undertook 20 weeks of supervised resistance-exercise training (RET). Expectedly, our subjects exhibited a marked range of hypertrophic responses (3% to +28%), and when applying Ingenuity Pathway Analysis (IPA) up-stream analysis to ∼580 genes that co-varied with gain in lean mass, we identified rapamycin (mTOR) signaling associating with growth (P = 1.4×10−30). Paradoxically, those displaying most hypertrophy exhibited an inhibited mTOR activation signature, including the striking down-regulation of 70 rRNAs. Differential analysis found networks mimicking developmental processes (activated all-trans-retinoic acid (ATRA, Z-score = 4.5; P = 6×10−13) and inhibited aryl-hydrocarbon receptor signaling (AhR, Z-score = −2.3; P = 3×10−7)) with RET. Intriguingly, as ATRA and AhR gene-sets were also a feature of endurance exercise training (EET), they appear to represent “generic” physical activity responsive gene-networks. For age, we found that differential gene-expression methods do not produce consistent molecular differences between young versus old individuals. Instead, utilizing two independent cohorts (n = 45 and n = 52), with a continuum of subject ages (18–78 y), the first reproducible set of age-related transcripts in human muscle was identified. This analysis identified ∼500 genes highly enriched in post-transcriptional processes (P = 1×10−6) and with negligible links to the aforementioned generic exercise regulated gene-sets and some overlap with ribosomal genes. The RNA signatures from multiple compounds all targeting serotonin, DNA topoisomerase antagonism, and RXR activation were significantly related to the muscle age-related genes. Finally, a number of specific chromosomal loci, including 1q12 and 13q21, contributed by more than chance to the age-related gene list (P = 0.01–0.005), implying possible epigenetic events. We conclude that human muscle age-related molecular processes appear distinct from the processes regulated by those of physical activity.

Phagosomes Induced by Cytokines Function as anti-Listeria Vaccines

Phagosomes are critical compartments for innate immunity. However, their role in the protection against murine listeriosis has not been examined. We describe here that listericidal phago-receptosomes are induced by the function of IFN-γ or IL-6 as centralized compartments for innate and adaptive immunity because they are able to confer protection against murine listeriosis. These phago-receptosomes elicited LLO(91-99)/CD8(+)- and LLO(189-201)/CD4(+)-specific immune responses and recruited mature dendritic cells to the vaccination sites controlled by T cells. Moreover, they present exceptional features as efficient vaccine vectors. First, they compartmentalize a novel listericidal STAT-1-mediated signaling pathway that confines multiple innate immune components to the same environment. Second, they show features of MHC class II antigen-loading competent compartments for cathepsin-D-mediated LLO processing. Third, murine cathepsin-D deficiencies fail to develop protective immunity after vaccination with listericidal phago-receptosomes induced by IFN-γ or IL-6. Therefore, it appears that the connection of STAT-1 and cathepsin-D in a single compartment is relevant for protection against listeriosis.

Oligonucleotide microarray expression analysis of genes whose expression is correlated with tumorigenic and non-tumorigenic phenotype of HeLa×human fibroblast hybrid cells

In order to understand the differences and similarities between tumorigenic and non-tumorigenic HeLa×human fibroblast hybrids, gene expression profiles were examined with synthetic oligonucleotide arrays containing nearly 7000 gene probe sets. We used two pairs of genetically related hybrids, each pair representing individual clones of non-tumorigenic and tumorigenic segregant hybrids, respectively. Analysis of six possible comparisons, utilizing two algorithms, identified 204 genes with differential expression. The greater number of differentially expressed genes was observed when non-tumorigenic hybrids were compared with tumorigenic segregants. Fifteen and 14 genes, respectively, were consistently found to be differentially expressed in non-tumorigenic and tumorigenic cells. Among those 29 differentially expressed genes, three ( intestinal alkaline phosphatase, caveolin-1, and solute carrier family2, member3) have been reported previously to be associated with the tumorigenic phenotype, using the same hybrid pairs. In addition, among the genes previously detected by differential display, 78% of them exhibited more than 5-fold change, demonstrating a high consistency between the two methods of differential gene expression. These findings suggest that synthetic oligonucleotide arrays are a powerful and highly reproducible tool to identify those genes whose expression is associated with certain phenotypes.

High throughput analysis of differential gene expression.

Elucidation of the changes in gene expression associated with biological processes is a central problem in biology. Advances in molecular and computational biology have led to the development of powerful, high-thoughput methods for the analysis of differential gene expression. These tools have opened up new opportunities in disciplines ranging from cell and developmental biology to drug development and pharmacogenomics. In this review, the attributes of five commonly used differential gene expression methods are discussed: expressed sequence tag (EST) sequencing, cDNA microarray hybridization, subtractive cloning, differential display, and serial analysis of gene expression (SAGE). The application of EST sequencing and microarray hybridization is illustrated by the discovery of novel genes associated with osteoblast differentiation. The application of subtractive cloning is presented as a tool to identify genes regulated in vivo by the transcription factor pax-6. These and other examples illustrate the power of genomics for discovering novel genes that are important in biology and which also represent new targets for drug development. The central theme of the review is that each of the approaches to identifying differentially expressed genes is useful, and that the experimental context and subsequent evaluation of differentially expressed genes are the critical features that determine success. J. Cell. Biochem. Suppls. 30/31:286-296, 1998. © 1998 Wiley-Liss, Inc.