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Related Concept Videos

Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Epistasis01:39

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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Linking gene expression to phenotypes via pathway information.

Irene Papatheodorou1, Anika Oellrich1, Damian Smedley1

  • 1Mouse Developmental Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB1 10SA, Hinxton, UK.

Journal of Biomedical Semantics
|April 23, 2015
PubMed
Summary
This summary is machine-generated.

Linking gene expression, pathways, and phenotypes is key for disease understanding and treatment. Computational methods and ontologies are advancing our ability to connect these elements for better biological insights.

Keywords:
Gene expressionOntologiesPathwaysPhenotypes

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Understanding the connection between genes, biological pathways, and observable traits (phenotypes) is crucial for disease research.
  • Computational approaches are increasingly used to analyze complex biological data.

Purpose of the Study:

  • To review current computational methods for linking gene expression, pathways, and phenotypes.
  • To identify future research directions for improved integration of these biological data types.

Main Methods:

  • Discussion of existing ontologies and controlled vocabularies.
  • Review of computational techniques for data integration and mining.
  • Analysis of methods connecting genes to expression, pathways, and phenotypes.

Main Results:

  • Numerous ontologies and computational tools have been developed to facilitate data integration.
  • Progress has been made in computational means to model pathways and classify phenotypes from gene data.

Conclusions:

  • Further integration of gene expression, pathway, and phenotype data is needed.
  • Enhanced computational strategies can provide deeper insights into disease mechanisms and gene mutation effects.