Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of ...genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use.
The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis
, three-dimensional morphological classification
and electron microscopy mapping of the connectome
...have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation.
Transcriptional enhancers function as docking platforms for combinations of transcription factors (TFs) to control gene expression. How enhancer sequences determine nucleosome occupancy, TF ...recruitment and transcriptional activation in vivo remains unclear. Using ATAC-seq across a panel of Drosophila inbred strains, we found that SNPs affecting binding sites of the TF Grainy head (Grh) causally determine the accessibility of epithelial enhancers. We show that deletion and ectopic expression of Grh cause loss and gain of DNA accessibility, respectively. However, although Grh binding is necessary for enhancer accessibility, it is insufficient to activate enhancers. Finally, we show that human Grh homologs-GRHL1, GRHL2 and GRHL3-function similarly. We conclude that Grh binding is necessary and sufficient for the opening of epithelial enhancers but not for their activation. Our data support a model positing that complex spatiotemporal expression patterns are controlled by regulatory hierarchies in which pioneer factors, such as Grh, establish tissue-specific accessible chromatin landscapes upon which other factors can act.
Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human ...curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.
Drosophila eye development is a complex process that involves many transcription factors (TFs) and interactions with their cofactors and targets. The TF Sine oculis (So) and its cofactor Eyes absent ...(Eya) are highly conserved and are both necessary and sufficient for eye development. Despite their many important roles during development, the direct targets of So are still largely unknown. Therefore the So-dependent regulatory network governing eye determination and differentiation is poorly understood. In this study, we intersected gene expression profiles of so or eya mutant eye tissue prepared from three different developmental stages and identified 1731 differentially expressed genes across the Drosophila genome. A combination of co-expression analyses and motif discovery identified a set of twelve putative direct So targets, including three known and nine novel targets. We also used our previous So ChIP-seq data to assess motif predictions for So and identified a canonical So binding motif. Finally, we performed in vivo enhancer reporter assays to test predicted enhancers from six candidate target genes and find that at least one enhancer from each gene is expressed in the developing eye disc and that their expression patterns overlap with that of So. In summary, we expand the set of putative So targets and show for the first time that the combined use of expression profiling of so with its cofactor eya is an effective method to identify novel So targets. Moreover, since So is highly conserved throughout the metazoa, our results provide the basis for future functional studies in a wide variety of organisms.
Drosophila eye development is a complex process that involves many transcription factors (TFs) and interactions with their cofactors and targets. The TF Sine oculis (So) and its cofactor Eyes absent ...(Eya) are highly conserved and are both necessary and sufficient for eye development. Despite their many important roles during development, the direct targets of So are still largely unknown. Therefore the So-dependent regulatory network governing eye determination and differentiation is poorly understood. In this study, we intersected gene expression profiles of so or eya mutant eye tissue prepared from three different developmental stages and identified 1731 differentially expressed genes across the Drosophila genome. A combination of co-expression analyses and motif discovery identified a set of twelve putative direct So targets, including three known and nine novel targets. We also used our previous So ChIP-seq data to assess motif predictions for So and identified a canonical So binding motif. Finally, we performed in vivo enhancer reporter assays to test predicted enhancers from six candidate target genes and find that at least one enhancer from each gene is expressed in the developing eye disc and that their expression patterns overlap with that of So. We furthermore confirmed that the expression level of predicted direct So targets, for which antibodies are available, are reduced in so or eya post-mitotic knockout eye discs. In summary, we expand the set of putative So targets and show for the first time that the combined use of expression profiling of so with its cofactor eya is an effective method to identify novel So targets. Moreover, since So is highly conserved throughout the metazoa, our results provide the basis for future functional studies in a wide variety of organisms.
•Determining the gene expression profiles of so or eya mutant eye tissues.•Applying co-expression analysis and motif discovery to differentially expressed genes.•A canonical So binding motif are identified from motif discovery and ChIP-seq data.•Enhancer reporter assays confirm predicted So binding motifs in vivo.
Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states ...providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes.
We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype.
The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The application of the bioinformatic tool here presented shows a neat way to achieve such molecular characterization of the diseases using genome-wide expression data.
Analysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of ...data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations.
Abstract 1704
Gene expression profiling studies have been performed in MDS to better characterize these diseases. However, the molecular pathogenesis of low-risk MDS is not yet fully understood. ...Furthermore, the transcriptional activity is dependent on many factors including epigenetic modifications. Therefore the integration of genome-wide epigenetic regulatory marks along with gene expression levels would provide additional information regarding the biological characteristics of low-risk MDS.
A total of 83 low-risk MDS patients and 36 age-matched controls were included in the study. A cohort of 18 patients with low-risk MDS and seven controls were included in a simultaneous integrative study of methylation and expression, while the whole series was used as a control group of expression data. Both the RNA and the DNA were isolated from BM mononucleate cells and hybridised with the Human Genome Expression Array (U133 Plus) from Affymetrix and MCAM Array from University Health Network (Canada), respectively. For analysis and interpretation of the hybridisation results, the R/Bioconductor program, DAVID bioinformatic resource, the web-delivered bioinformatics tool set Ingenuity Pathway Analysis and Metacore Analytical Suite were used. The results generated by expression and methylation microarrays were confirmed using Q- PCR and pyrosequencing, respectively.
A total of 817 differentially methylated genes were identified as being present in low-risk MDS (p< 0.10); hyper-methylated genes (n=457) were more frequent than hypo-methylated genes (n=360). In addition, mRNA expression profiling identified 1005 genes that significantly differed between low-risk MDS and control group. Integrative analysis of the epigenetic and expression profiles revealed that 66.7% of the hyper-methylated genes were under-expressed in low-risk MDS cases.
The most represented categories were regulation of apoptosis, gene expression, immune response and RNA process. BCL2, ETS1, IL27RA and DICER1, all of them hyper-methylated and down-expressed, were the most significant genes related to these functions. 1. Regarding apoptosis and BCL2, an over-expression of BCL2L11 and MYC were found in low-risk MDS. In contrast, BAX and CUX1 were under-expressed with respect to the control group. In addition, SYK gene was also hyper-methylated and under-expressed. 2. Promoter region analysis demonstrated that ETS1 transcription factor was involved in the regulation of 83 target genes included in the down-regulation signature of the low-risk MDS patients. The most significant functions of these target genes revealed that the cell-to-cell signaling and interaction pathway were prominently affected. In addition, apoptosis was identified as the function with the most number of down-regulated target genes. Therefore, the overall apoptosis pathway could be affected in low-risk MDS patients in two ways: methylation and decreased expression of BCL2 with the deregulation of related genes, as well as methylation and decreased expression of the ETS1 transcription factor with the deregulation of the apoptosis-related targets. 3. Regarding immune response, the study showed that besides IL27RA, another nine interleukins and interleukin receptors were under-expressed in the same cohort of patients: IL16, IL32, IL1RAP, IL2RB, IL6R, IL7R, IL10RA, IL10RB and IL13RA1. Three of them (IL16, IL1RAP and IL10RB) had direct genetic interactions with IL27RA. 4. Finally, the identification of DICER1 as a gene significantly altered by methylation and expression in low-risk MDS prompted us to measure the 183 miRNAs expression. A general down-regulation of miRNAs was observed in low-risk MDS cases respect to the control group (p=0.039).
Our integrative analysis revealed that aberrant epigenetic regulation is a hallmark of low-risk MDS patients and could play a central role in these diseases. Furthermore, we highlight candidate DNA methylation changes associated with low-risk MDS patients.
No relevant conflicts of interest to declare.
PDF
