scholarly journals Rule-based Clustering for Gene Promoter Structure Discovery

2009 ◽  
Vol 48 (03) ◽  
pp. 229-235
Author(s):  
U. Petrovic ◽  
G. Shaulsky ◽  
B. Zupan ◽  
T. Curk
Keyword(s):  
Gene Expression ◽  
Cellular Response ◽  
Expression Profiles ◽  
Gene Promoter ◽  
Regulatory Elements ◽  
Experiment Planning ◽  
Learning Approaches ◽  
Promoter Regions ◽  
Rule Based ◽  
Gene Regulatory

Summary Background: The genetic cellular response to internal and external changes is determined by the sequence and structure of gene-regulatory promoter regions. Objectives: Using data on gene-regulatory elements (i.e., either putative or known transcription factor binding sites) and data on gene expression profiles we can discover structural elements in promoter regions and infer the underlying programs of gene regulation. Such hypotheses obtained in silico can greatly assist us in experiment planning. The principal obstacle for such approaches is the combinatorial explosion in different combinations of promoter elements to be examined. Methods: Stemming from several state-ofthe-art machine learning approaches we here propose a heuristic, rule-based clustering method that uses gene expression similarity to guide the search for informative structures in promoters, thus exploring only the most promising parts of the vast and expressively rich rule-space. Results: We present the utility of the method in the analysis of gene expression data on budding yeast S. cerevisiae where cells were induced to proliferate peroxisomes. Conclusions: We demonstrate that the proposed approach is able to infer informative relations uncovering relatively complex structures in gene promoter regions that regulate gene expression.

scholarly journals Binding of an X-Specific Condensin Correlates with a Reduction in Active Histone Modifications at Gene Regulatory Elements

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 729-742 ◽  
Author(s):  
Lena Annika Street ◽  
Ana Karina Morao ◽  
Lara Heermans Winterkorn ◽  
Chen-Yu Jiao ◽  
Sarah Elizabeth Albritton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Histone Modifications ◽  
Chromatin Immunoprecipitation ◽  
Protein Complexes ◽  
Regulatory Elements ◽  
Evolutionarily Conserved ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Gene Regulatory Elements ◽  
Gene Regulatory ◽  
Regulatory Sites

Condensins are evolutionarily conserved protein complexes that are required for chromosome segregation during cell division and genome organization during interphase. In Caenorhabditis elegans, a specialized condensin, which forms the core of the dosage compensation complex (DCC), binds to and represses X chromosome transcription. Here, we analyzed DCC localization and the effect of DCC depletion on histone modifications, transcription factor binding, and gene expression using chromatin immunoprecipitation sequencing and mRNA sequencing. Across the X, the DCC accumulates at accessible gene regulatory sites in active chromatin and not heterochromatin. The DCC is required for reducing the levels of activating histone modifications, including H3K4me3 and H3K27ac, but not repressive modification H3K9me3. In X-to-autosome fusion chromosomes, DCC spreading into the autosomal sequences locally reduces gene expression, thus establishing a direct link between DCC binding and repression. Together, our results indicate that DCC-mediated transcription repression is associated with a reduction in the activity of X chromosomal gene regulatory elements.

Gene-regulatory elements may change the amount, timing, or location of gene expression, cis-Regulation therapy platforms might become a gene therapy to treat many genetic diseases

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Gene Expression ◽  
Genetic Diseases ◽  
Regulatory Elements ◽  
Tissue Type ◽  
Gene Promoters ◽  
Expression Levels ◽  
Cis Regulation ◽  
Gene Regulatory Elements ◽  
Gene Regulatory ◽  
Gene Expression Levels

Changes in gene expression levels above or below a particular threshold may have a dramatic impact on phenotypes, leading to a wide spectrum of human illnesses. Gene-regulatory elements, also known as cis-regulatory elements (CREs), may change the amount, timing, or location (cell/tissue type) of gene expression, whereas mutations in a gene's coding sequence may result in lower or higher gene expression levels resulting in protein loss or gain. Loss-of-function mutations in both genes produce recessive human illness, while haploinsufficient mutations in 65 genes are also known to be deleterious due to function gain, according to the ClinVar1 and ClinGen3 databases. CREs are promoters living near to a gene's transcription start site and switching it on at predefined times, places, and levels. Other distal CREs, like enhancers and silencers, are temporal and tissue-specific control promoters. Enhancers activate promoters, commonly referred to as "promoters," whereas silencers turn them off. Insulators also restrict promiscuous interactions between enhancers and gene promoters. Systematic genomic approaches can help understand the cis-regulatory circuitry of gene expression by highly detecting and functionally defining these CREs. This includes the new use of CRISPR–CRISPR-associated protein 9 (CRISPR–Cas9) and other editing approaches to discover CREs. Cis-Regulation therapy (CRT) provides many promises to heal human ailments. CRT may be used to upregulate or downregulate disease-causing genes due to lower or higher levels of expression, and it may also be used to precisely adjust the expression of genes that assist in alleviating disease features. CRT may employ proteins that generate epigenetic modifications like methylation, histone modification, or gene expression regulation looping. Weighing CRT's advantages and downsides against alternative treatment methods is crucial. CRT platforms might become a practical technique to treat many genetic diseases that now lack treatment alternatives if academics, patient communities, clinicians, regulators and industry work together.

scholarly journals Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Developmental Time ◽  
Floral Transition ◽  
Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

scholarly journals Identification of key biomarkers associated with development and prognosis in patients with ovarian carcinoma: evidence from bioinformatic analysis

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiayu Shen ◽  
Shuqian Yu ◽  
Xiwen Sun ◽  
Meichen Yin ◽  
Jing Fei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Adhesion ◽  
Adhesion Molecules ◽  
Transforming Growth Factor Beta ◽  
Cell Adhesion Molecules ◽  
Cellular Response ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Regulation Of Transcription ◽  
Hub Genes

Abstract Background Ovarian cancer (OC) is the deadliest cause in the gynecological malignancies. Most OC patients are diagnosed in advanced stages with less than 40% of women cured. However, the possible mechanism underlying tumorigenesis and candidate biomarkers remain to be further elucidated. Results Gene expression profiles of GSE18520, GSE54388, and GSE27651 were available from Gene Expression Omnibus (GEO) database with a total of 91 OC samples and 22 normal ovarian (OV) tissues. Three hundred forty-nine differentially expressed genes (DEGs) were screened between OC tissues and OV tissues via GEO2R and online Venn software, followed by KEGG pathway and gene ontology (GO) enrichment analysis. The enriched functions and pathways of these DEGs contain male gonad development, cellular response to transforming growth factor beta stimulus, positive regulation of transcription from RNA polymerase II promoter, calcium independent cell-cell adhesion via plasma membrane cell adhesion molecules, extracellular matrix organization, pathways in cancer, cell cycle, cell adhesion molecules, PI3K-AKT signaling pathway, and progesterone mediated oocyte maturation. The protein-protein network (PPI) was established and module analysis was carried out using STRING and Cytoscape. Next, with PPI network analyzed by four topological methods in Cytohubba plugin of Cytoscape, 6 overlapping genes (DTL, DLGAP5, KIF15, NUSAP1, RRM2, and TOP2A) were eventually selected. GEPIA and Oncomine were implemented for validating the gene expression and all the six hub genes were highly expressed in OC specimens compared to normal OV tissues. Furthermore, 5 of 6 genes except for DTL were associated with worse prognosis using Kaplan Meier-plotter online tool and 3 of 6 genes were significantly related to clinical stages, including RRM2, DTL, and KIF15. Additionally, cBioPortal showed that TOP2A and RRM2 were the targets of cancer drugs in patients with OC, indicating the other four genes may also be potential drug targets. Conclusion Six hub genes (DTL, DLGAP5, KIF15, NUSAP1, RRM2, and TOP2A) present promising predictive value for the development and prognosis of OC and may be used as candidate targets for diagnosis and treatment of OC.

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

Mechanisms of mammalian zinc-regulated gene expression

2008 ◽  
Vol 36 (6) ◽  
pp. 1262-1266 ◽  
Author(s):  
Kelly A. Jackson ◽  
Ruth A. Valentine ◽  
Lisa J. Coneyworth ◽  
John C. Mathers ◽  
Dianne Ford
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Repression ◽  
Mammalian Cells ◽  
Gene Promoter ◽  
Mrna Levels ◽  
Promoter Regions ◽  
Mrna Stabilization ◽  
Transcriptional Regulatory ◽  
Regulated Gene Expression

Mechanisms through which gene expression is regulated by zinc are central to cellular zinc homoeostasis. In this context, evidence for the involvement of zinc dyshomoeostasis in the aetiology of diseases, including Type 2 diabetes, Alzheimer's disease and cancer, highlights the importance of zinc-regulated gene expression. Mechanisms elucidated in bacteria and yeast provide examples of different possible modes of zinc-sensitive gene regulation, involving the zinc-regulated binding of transcriptional activators and repressors to gene promoter regions. A mammalian transcriptional regulatory mechanism that mediates zinc-induced transcriptional up-regulation, involving the transcription factor MTF1 (metal-response element-binding transcription factor 1), has been studied extensively. Gene responses in the opposite direction (reduced mRNA levels in response to increased zinc availability) have been observed in mammalian cells, but a specific transcriptional regulatory process responsible for such a response has yet to be identified. Examples of single zinc-sensitive transcription factors regulating gene expression in opposite directions are emerging. Although zinc-induced transcriptional repression by MTF1 is a possible explanation in some specific instances, such a mechanism cannot account for repression by zinc of all mammalian genes that show this mode of regulation, indicating the existence of as yet uncharacterized mechanisms of zinc-regulated transcription in mammalian cells. In addition, recent findings reveal a role for effects of zinc on mRNA stability in the regulation of specific zinc transporters. Our studies on the regulation of the human gene SLC30A5 (solute carrier 30A5), which codes for the zinc transporter ZnT5, have revealed that this gene provides a model system by which to study both zinc-induced transcriptional down-regulation and zinc-regulated mRNA stabilization.

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