Dataset Information

Mapping of the transcription start site (TSS) and identification of SNPs in the bovine neuropeptide Y (NPY) gene.

ABSTRACT: BACKGROUND: Neuropeptide Y is a key neurotransmitter of the central nervous system which plays a vital role in the feed energy homeostasis in mammals. Mutations in the regulatory and coding regions of the bovine NPY gene can potentially affect the neuronal regulation of appetite and feeding behaviour in cattle. The objectives of this experiment were to: a) fully characterize the bovine NPY gene transcript and b) identify the SNP diversity in both coding and non-coding regions of the NPY gene in a panel of Bos taurus and B. indicus cattle. RESULTS: Bovine NPY gene consists of four exons (99, 188, 81 and 195 nucleotides) and three introns. The promoter region of the NPY gene consists of TATA and GC boxes which are separated from the transcription start site (TSS) by 29 and ~100 nt, respectively. Analyses of the tissue specific expression of the bovine NPY gene revealed the presence of highly abundant NPY gene transcripts in the arcuate nucleus, cerebral and cerebellar regions of the bovine brain. We identified a total of 59 SNPs in the 8.4 kb region of the bovine NPY gene. Seven out of nine total SNPs in the promoter region affect binding of putative transcription factors. A high level of nucleotide diversity was evident in the promoter regions (2.84 x 10(-3)) compared to the exonic (1.44 x 10(-3)), intronic (1.30 x 10(-3)) and 3' untranslated (1.26 x 10(-3)) regions. CONCLUSION: The SNPs identified in different regions of bovine NPY gene may serve as a basis for understanding the regulation of the expression of the bovine NPY gene under a variety of physiological conditions and identification of genotypes with high feed energy conversion efficiency.

SUBMITTER: Bahar B

PROVIDER: S-EPMC2657160 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mapping of the transcription start site (TSS) and identification of SNPs in the bovine neuropeptide Y (NPY) gene.

Bahar Bojlul B Sweeney Torres T

BMC genetics 20081223

<h4>Background</h4>Neuropeptide Y is a key neurotransmitter of the central nervous system which plays a vital role in the feed energy homeostasis in mammals. Mutations in the regulatory and coding regions of the bovine NPY gene can potentially affect the neuronal regulation of appetite and feeding behaviour in cattle. The objectives of this experiment were to: a) fully characterize the bovine NPY gene transcript and b) identify the SNP diversity in both coding and non-coding regions of the NPY g ...[more]

PMID: 19105820

Similar Datasets

Project description:Bacteria rely on efficient gene regulatory mechanisms to switch between genetic programs when they are facing new environments. Although this regulation can occur at many different levels, one of the key steps is the initiation of transcription. Identification of the first nucleotide transcribed by the RNA polymerase is therefore essential to understand the underlying regulatory processes, since this provides insight on promoter strength and binding sites for transcriptional regulators, and additionally reveals the exact 5' untranslated region of the transcripts, which often contains elements that regulate translation.Here we present data from a novel TSS-EMOTE assay (Transcription Start Specific Exact Mapping Of Transcriptome Ends) to precisely map the transcription initiation sites of four entire transcriptomes. TSS-EMOTE is a variation of the dRNA-seq method, which has been combined with the EMOTE protocol, in order to increase detection of longer transcripts and limit biases introduced by PCR amplification of the Illumina sequencing library. Using TSS-EMOTE, 2018 promoters were detected in the opportunistic pathogen Staphylococcus aureus, and detailed consensus sequences could be obtained for the RNA polymerase recognition elements (e.g. sigma factor binding sites). The data also revealed a 94 nt median length of the 5' untranslated region in S. aureus, giving important insights for future work on translational regulation. Additionally, the transcriptomes of three other opportunistic pathogens, Staphylococcus epidermidis, Acinetobacter baumannii and Enterobacter aerogenes, were examined, and the identified promoter locations were then used to generate a map of the operon structure for each of the four organisms.Mapping transcription start sites, and subsequent correlation with the genomic sequence, provides a multitude of important information about the regulation of gene expression, both at the transcriptional and translational level, by defining 5' untranslated regions and sigma-factor binding sites. We have here mapped transcription start sites in four important pathogens using TSS-EMOTE, a method that works with both long and 3'-phosphorylated RNA molecules, and which incorporates Unique Molecular Identifiers (UMIs) to allow unbiased quantification.

Project description:Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.

Project description:BackgroundThe genetic program, as manifested as the cellular phenotype, is in large part dictated by the cell's protein composition. Since characterisation of the proteome remains technically laborious it is attractive to define the genetic expression profile using the transcriptome. However, the transcriptional landscape is complex and it is unclear as to what extent it reflects the ribosome associated mRNA population (the translatome). This is particularly pertinent for genes using multiple transcriptional start sites (TSS) generating mRNAs with heterogeneous 5' transcript leaders (5'TL). Furthermore, the relative abundance of the TSS gene variants is frequently cell-type specific. Indeed, promoter switches have been reported in pathologies such as cancer. The consequences of this 5'TL heterogeneity within the transcriptome for the translatome remain unresolved. This is not a moot point because the 5'TL plays a key role in regulating mRNA recruitment onto polysomes.ResultsIn this article, we have characterised both the transcriptome and translatome of the MCF7 (tumoural) and MCF10A (non-tumoural) cell lines. We identified ~550 genes exhibiting differential translation efficiency (TE). In itself, this is maybe not surprising. However, by focusing on genes exhibiting TSS heterogeneity we observed distinct differential promoter usage patterns in both the transcriptome and translatome. Only a minor fraction of these genes belonged to those exhibiting differential TE. Nonetheless, reporter assays demonstrated that the TSS variants impacted on the translational readout both quantitatively (the overall amount of protein expressed) and qualitatively (the nature of the proteins expressed).ConclusionsThe results point to considerable and distinct cell-specific 5'TL heterogeneity within both the transcriptome and translatome of the two cell lines analysed. This observation is in-line with the ribosome filter hypothesis which posits that the ribosomal machine can selectively filter information from within the transcriptome. As such it cautions against the simple extrapolation transcriptome → proteome. Furthermore, polysomal occupancy of specific gene 5'TL variants may also serve as novel disease biomarkers.

Dataset Information

Mapping of the transcription start site (TSS) and identification of SNPs in the bovine neuropeptide Y (NPY) gene.

Publications

Mapping of the transcription start site (TSS) and identification of SNPs in the bovine neuropeptide Y (NPY) gene.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets