Exon Level Machine Learning Analyses Elucidate Novel Candidate miRNA Targets in an Avian Model of Fetal Alcohol Spectrum Disorder
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ABSTRACT:
ORGANISM(S): Gallus gallus
PROVIDER: GSE115383 | GEO | 2018/08/01
REPOSITORIES: GEO
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2010-06-24 | E-GEOD-14792 | biostudies-arrayexpress
Project description:Chicken eggs (Gallus gallus domesticus) were incubated, after 24 h of incubation either non-injected (control, C), or injected with the solvent sesame oil (Oleum Sesame Raffinatum, solvent control, SC), and the substances Tributyltin (TBT, 10pg TBT-SN/g egg) or 17alpha-Methyltestosteron (MT, 30 pg/g egg). Animals were decapitated on breeding day 19, 2 days before anticipated hatching. Right and left gonads of female and male chicken were accurately separated from adhesing tissues, stored in RNA lysis buffer (Promega) at -80°C until until RNA isolation with the SV Total RNA Isolation System Kit according to manual 048 (Promega). Sequencing was performed on Illumina’s Genome Analyzer IIx, and subsequent base calling was carried out by Illumina’s GAPipeline. Our study represents the first detailed analysis of whole chicken gonad transcriptomes, with biologic replicates, generated by SuperSAGE technology.
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Project description:Exon Level Machine Learning Analyses Elucidate Novel Candidate miRNA Targets in an Avian Model of Fetal Alcohol Spectrum Disorder
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Project description:Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis provides limited information regarding complex transcriptional processes such as alternative splicing and non-coding RNAs. Moreover, traditional analytical approaches that use multiple hypothesis testing with a false discovery rate adjustment prioritize genes based on an adjusted p-value, which is not always biologically relevant. We address these limitations with a novel approach and implemented an unsupervised machine learning model, which we applied to an exon-level analysis to reduce data complexity to the most likely functionally relevant exons, without loss of novel information. This was performed on an RNA-Seq paired-end dataset derived from alcohol-exposed neural fold-stage chick crania, wherein alcohol causes facial deficits recapitulating those of FASD. A principal component analysis along with k-means clustering was utilized to extract exons that deviated from baseline expression. This identified 6857 differentially expressed exons representing 1251 geneIDs; 391 of these genes were identified in a prior gene-level analysis of this dataset. It also identified exons encoding 23 microRNAs (miRNAs) having significantly differential expression profiles in response to alcohol. We developed an RDAVID pipeline to identify KEGG pathways represented by these exons, and separately identified predicted KEGG pathways targeted by these miRNAs. Several of these (ribosome biogenesis, oxidative phosphorylation) were identified in our prior gene-level analysis. Other pathways are crucial to facial morphogenesis and represent both novel (focal adhesion, FoxO signaling, insulin signaling) and known (Wnt signaling) alcohol targets. Importantly, there was substantial overlap between the exomes themselves and the predicted miRNA targets, suggesting these miRNAs contribute to the gene-level expression changes. Our novel application of unsupervised machine learning in conjunction with statistical analyses facilitated the discovery of signaling pathways and miRNAs that inform mechanisms underlying FASD.
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Project description:Purpose: Avian photoreceptors are a diverse class of neurons, comprised of four single cones, the two members of the double cone, and rods. Many distinctive features of photoreceptor subtypes, including spectral tuning, oil droplet size and pigmentation, synaptic targets and spatial patterning, have been well characterized, but the molecular mechanisms underlying these attributes have not been explored. Furthermore, the signaling events and transcriptional regulators driving the differentiation of these diverse photoreceptors are currently unknown. Methods: To identify genes specifically expressed in distinct chicken (Gallus gallus) photoreceptor subtypes, we developed fluorescent reporters that label photoreceptor subpopulations, isolated these subpopulations using fluorescence-activated cell sorting, subjected them to next-generation sequencing, and conducted differential expression analysis. Results: We identified hundreds of differentially expressed genes from photoreceptor subpopulations labeled with rhodopsin, red opsin, green opsin, and violet opsin reporters. These genes are involved in a variety of processes, including phototransduction, transcriptional regulation, cell adhesion, maintenance of intra- and extra-cellular structure, and metabolism. Of particular note are a variety of differentially expressed transcription factors, which may drive and maintain photoreceptor diversity, and cell adhesion molecules that may mediate spatial patterning of photoreceptors and act to establish retinal circuitry. Conclusions: These analyses provide a framework for future studies that will dissect the role of these various factors in the differentiation of avian photoreceptor subtypes.
2014-09-01 | GSE59850 | GEO
Project description:Purpose: Avian photoreceptors are a diverse class of neurons, comprised of four single cones, the two members of the double cone, and rods. Many distinctive features of photoreceptor subtypes, including spectral tuning, oil droplet size and pigmentation, synaptic targets and spatial patterning, have been well characterized, but the molecular mechanisms underlying these attributes have not been explored. Furthermore, the signaling events and transcriptional regulators driving the differentiation of these diverse photoreceptors are currently unknown. Methods: To identify genes specifically expressed in distinct chicken (Gallus gallus) photoreceptor subtypes, we developed fluorescent reporters that label photoreceptor subpopulations, isolated these subpopulations using fluorescence-activated cell sorting, subjected them to next-generation sequencing, and conducted differential expression analysis. Results: We identified hundreds of differentially expressed genes from photoreceptor subpopulations labeled with rhodopsin, red opsin, green opsin, and violet opsin reporters. These genes are involved in a variety of processes, including phototransduction, transcriptional regulation, cell adhesion, maintenance of intra- and extra-cellular structure, and metabolism. Of particular note are a variety of differentially expressed transcription factors, which may drive and maintain photoreceptor diversity, and cell adhesion molecules that may mediate spatial patterning of photoreceptors and act to establish retinal circuitry. Conclusions: These analyses provide a framework for future studies that will dissect the role of these various factors in the differentiation of avian photoreceptor subtypes. mRNA expression profiling of 5 pairs of photoreceptor subtypes isolated from chicken retinal explants, 3 replicates per sample
2014-09-01 | E-GEOD-59850 | biostudies-arrayexpress
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2020-09-24 | GSE158417 | GEO
Project description:Gene regulatory elements are central drivers of phenotypic variation and thus of critical importance towards understanding the genetics of complex traits. Here we present a genome-wide annotation of regulatory elements in a non-mammalian vertebrate, chicken (Gallus gallus), as well as two important agricultural mammalian species: pig (Sus scrofa) and cattle (Bos taurus), with chicken and pig in particular being important to human biology and medicine. This report is the first to employ all core assays as defined by the Functional Annotation of Animal Genomes (FAANG) consortium, including information from a wide range of epigenomic assays for the same eight diverse tissues of three livestock species. Comparative analysis of these datasets and those from the human and mouse ENCODE projects revealed that although less than half of enhancers are positionally conserved between species, a core set of regulatory elements are functionally conserved independent of evolutionary distance. Further analysis suggested that tissue-specific transcription factor occupancy at regulatory elements and their predicted target genes were also conserved. Interestingly, the smaller chicken genome – relative to mammals – contains a reduced number of enhancers; however, each chicken enhancer targets more genes, on average, compared to their mammalian counterparts suggesting higher versatility. These datasets and corresponding analysis represent a unique opportunity for the emerging field of comparative epigenomics, as well as animal and human biology and medical research involving species that are globally important food resources.
2020-09-24 | GSE158414 | GEO
Project description:Gene regulatory elements are central drivers of phenotypic variation and thus of critical importance towards understanding the genetics of complex traits. Here we present a genome-wide annotation of regulatory elements in a non-mammalian vertebrate, chicken (Gallus gallus), as well as two important agricultural mammalian species: pig (Sus scrofa) and cattle (Bos taurus), with chicken and pig in particular being important to human biology and medicine. This report is the first to employ all core assays as defined by the Functional Annotation of Animal Genomes (FAANG) consortium, including information from a wide range of epigenomic assays for the same eight diverse tissues of three livestock species. Comparative analysis of these datasets and those from the human and mouse ENCODE projects revealed that although less than half of enhancers are positionally conserved between species, a core set of regulatory elements are functionally conserved independent of evolutionary distance. Further analysis suggested that tissue-specific transcription factor occupancy at regulatory elements and their predicted target genes were also conserved. Interestingly, the smaller chicken genome – relative to mammals – contains a reduced number of enhancers; however, each chicken enhancer targets more genes, on average, compared to their mammalian counterparts suggesting higher versatility. These datasets and corresponding analysis represent a unique opportunity for the emerging field of comparative epigenomics, as well as animal and human biology and medical research involving species that are globally important food resources.
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