Project description:BACKGROUND: A means to predict the effects of gene over-expression, knockouts, and environmental stimuli in silico is useful for system biologists to develop and test hypotheses. Several studies had predicted the expression of all Escherichia coli genes from sequences and reported a correlation of 0.301 between predicted and actual expression. However, these do not allow biologists to study the effects of gene perturbations on the native transcriptome. RESULTS: We developed a predictor to predict transcriptome-scale gene expression from a small number (n?=?59) of known gene expressions using gene co-expression network, which can be used to predict the effects of over-expressions and knockdowns on E. coli transcriptome. In terms of transcriptome prediction, our results show that the correlation between predicted and actual expression value is 0.467, which is similar to the microarray intra-array variation (p-value?=?0.348), suggesting that intra-array variation accounts for a substantial portion of the transcriptome prediction error. In terms of predicting the effects of gene perturbation(s), our results suggest that the expression of 83% of the genes affected by perturbation can be predicted within 40% of error and the correlation between predicted and actual expression values among the affected genes to be 0.698. With the ability to predict the effects of gene perturbations, we demonstrated that our predictor has the potential to estimate the effects of varying gene expression level on the native transcriptome. CONCLUSION: We present a potential means to predict an entire transcriptome and a tool to estimate the effects of gene perturbations for E. coli, which will aid biologists in hypothesis development. This study forms the baseline for future work in using gene co-expression network for gene expression prediction.
Project description:BACKGROUND:Next-generation sequencing is providing researchers with a relatively fast and affordable option for developing genomic resources for organisms that are not among the traditional genetic models. Here we present a de novo assembly of the guppy (Poecilia reticulata) transcriptome using 454 sequence reads, and we evaluate potential uses of this transcriptome, including detection of sex-specific transcripts and deployment as a reference for gene expression analysis in guppies and a related species. Guppies have been model organisms in ecology, evolutionary biology, and animal behaviour for over 100 years. An annotated transcriptome and other genomic tools will facilitate understanding the genetic and molecular bases of adaptation and variation in a vertebrate species with a uniquely well known natural history. RESULTS:We generated approximately 336 Mbp of mRNA sequence data from male brain, male body, female brain, and female body. The resulting 1,162,670 reads assembled into 54,921 contigs, creating a reference transcriptome for the guppy with an average read depth of 28×. We annotated nearly 40% of this reference transcriptome by searching protein and gene ontology databases. Using this annotated transcriptome database, we identified candidate genes of interest to the guppy research community, putative single nucleotide polymorphisms (SNPs), and male-specific expressed genes. We also showed that our reference transcriptome can be used for RNA-sequencing-based analysis of differential gene expression. We identified transcripts that, in juveniles, are regulated differently in the presence and absence of an important predator, Rivulus hartii, including two genes implicated in stress response. For each sample in the RNA-seq study, >50% of high-quality reads mapped to unique sequences in the reference database with high confidence. In addition, we evaluated the use of the guppy reference transcriptome for gene expression analyses in a congeneric species, the sailfin molly (Poecilia latipinna). Over 40% of reads from the sailfin molly sample aligned to the guppy transcriptome. CONCLUSIONS:We show that next-generation sequencing provided a reliable and broad reference transcriptome. This resource allowed us to identify candidate gene variants, SNPs in coding regions, and sex-specific gene expression, and permitted quantitative analysis of differential gene expression.
Project description:Objective: This study aimed to evaluate the effect of dendritic cell (DC) vaccination against HIV-1 on host gene expression profiles. Design: Longitudinal PBMC samples were collected from participants of the DC-TRN trial for immunotherapy against HIV. Microarray-assisted gene expression profiling was performed to evaluate the effects of vaccination and subsequent interruption of antiretroviral therapy on host genome expression. Data from the DC-TRN trial were compared with results from other vaccination trials. Methods: We used Affymetrix GeneChips for microarray gene expression analysis. Data were analyzed by principal component analysis and differential gene expression was assessed using linear modeling. Gene ontology enrichment and gene set analysis were used to characterize differentially expressed genes. Transcriptome analysis included comparison with PBMCs obtained from DC-vaccinated melanoma patients and of healthy individuals who received seasonal influenza vaccination. Results: DC-TRN immunotherapy in HIV-infected individuals resulted in a major shift in the transcriptome. Longitudinal analysis demonstrated that changes in the transcriptome sustained also during interruption of antiretroviral therapy. After DC-vaccination, the transcriptome was enriched for cellular immunity associated genes that were also induced in healthy adults who received live attenuated influenza virus vaccination. These beneficial responses were accompanied by detrimental signals of general immune activation. Conclusions: The DC-TRN induced changes in the transcriptome were profound, lasting, and consisted of both protective signals and signatures of inflammation and immune exhaustion, with a net result of decreased viral load, without clinical benefit. Thus transcriptome analysis provides useful information, dissecting both positive and negative effects, for the evaluation of safety and efficacy of immunotherapeutic strategies.
Project description:Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.Here, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression.Our method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation.
Project description:The transcriptome is the mRNA transcript pool in a cell, organ or tissue with the liver transcriptome being amongst the most complex of any organ. Functional genomics methodologies are now being widely utilized to study transcriptomes including the hepatic transcriptome. This review outlines commonly used methods of transcriptome analysis, especially gene array analysis, focusing on publications utilizing these methods to understand human liver disease. Additionally, we have outlined the relationship between transcript and protein expressions as well as summarizing what is known about the variability of the transcriptome in non-diseased liver tissue. The approaches covered include gene array analysis, serial analysis of gene expression, subtractive hybridization and differential display. The discussion focuses on primate whole organ studies and in-vitro cell culture systems utilized. It is now clear that there are a vast number research opportunities for transcriptome analysis of human liver disease as we attempt to better understand both non-diseased and disease hepatic mRNA expression. We conclude that hepatic transcriptome analysis has already made significant contributions to the understanding of human liver pathobiology.
Project description:Transcriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we generate a small but significant improvement in the accuracy of transcriptome prediction and increase the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge on biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify compounds that mimic, or reverse, trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.
Project description:We evaluate the feasibility of using a biological sample's transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we found that to a large extent gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element's neighboring genes. We show applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome-transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.
Project description:BACKGROUND: The brown planthopper (BPH) Nilaparvata lugens (Stål) is one of the most serious insect pests of rice in Asia. However, little is known about the mechanisms responsible for the development, wing dimorphism and sex difference in this species. Genomic information for BPH is currently unavailable, and, therefore, transcriptome and expression profiling data for this species are needed as an important resource to better understand the biological mechanisms of BPH. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we performed de novo transcriptome assembly and gene expression analysis using short-read sequencing technology (Illumina) combined with a tag-based digital gene expression (DGE) system. The transcriptome analysis assembles the gene information for different developmental stages, sexes and wing forms of BPH. In addition, we constructed six DGE libraries: eggs, second instar nymphs, fifth instar nymphs, brachypterous female adults, macropterous female adults and macropterous male adults. Illumina sequencing revealed 85,526 unigenes, including 13,102 clusters and 72,424 singletons. Transcriptome sequences larger than 350 bp were subjected to Gene Orthology (GO) and KEGG Orthology (KO) annotations. To analyze the DGE profiling, we mainly compared the gene expression variations between eggs and second instar nymphs; second and fifth instar nymphs; fifth instar nymphs and three types of adults; brachypterous and macropterous female adults as well as macropterous female and male adults. Thousands of genes showed significantly different expression levels based on the various comparisons. And we randomly selected some genes to confirm their altered expression levels by quantitative real-time PCR (qRT-PCR). CONCLUSIONS/SIGNIFICANCE: The obtained BPH transcriptome and DGE profiling data provide comprehensive gene expression information at the transcriptional level that could facilitate our understanding of the molecular mechanisms from various physiological aspects including development, wing dimorphism and sex difference in BPH.
Project description:Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.
Project description:Co-expression networks and gene regulatory networks (GRNs) are emerging as important tools for predicting the functional roles of individual genes at a system-wide scale. To enable network reconstructions we built a large-scale gene expression atlas comprised of 62,547 mRNAs, 17,862 non-modified proteins, and 6,227 phosphoproteins harboring 31,595 phosphorylation sites quantified across maize development. There was little edge conservation in co-expression and GRNs reconstructed using transcriptome versus proteome data yet networks from either data type were enriched in ontological categories and effective in predicting known regulatory relationships. This integrated gene expression atlas provides a valuable community resource. The networks should facilitate plant biology research and they provide a conceptual framework for future systems biology studies highlighting the importance of studying gene regulation at several levels.