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Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 1
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Detecting virus-specific effects on post-infection temporal gene expression.

Quan Chen1,2, Jun Zhu3,4,5

  • 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.

BMC Bioinformatics
|March 31, 2019
PubMed
Summary
This summary is machine-generated.

A new method, Multivariate Polynomial Time-dependent Genetic Association (MPTGA), effectively identifies virus-specific gene expression differences in mouse lungs. This approach offers greater power and biological relevance than traditional t-tests for studying dynamic viral responses.

Keywords:
Dynamic responseFlu virusTemporal association

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Vaccinia Virus Infection & Temporal Analysis of Virus Gene Expression: Part 2
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Area of Science:

  • Virology
  • Genomics
  • Bioinformatics

Background:

  • Viral envelope proteins influence host-virus interactions and post-infection effects.
  • Virus-induced effects manifest dynamically over time.
  • Understanding virus-specific responses is crucial for disease characterization.

Purpose of the Study:

  • To apply and evaluate the Multivariate Polynomial Time-dependent Genetic Association (MPTGA) method for analyzing mouse lung transcriptome responses to different influenza A virus subtypes.
  • To compare the efficacy of MPTGA against conventional statistical methods in detecting virus-specific gene expression patterns.

Main Methods:

  • Utilized the Multivariate Polynomial Time-dependent Genetic Association (MPTGA) method.
  • Analyzed mouse lung transcriptome data following infection with different influenza A virus subtypes.
  • Compared MPTGA with a conventional modified t-test.

Main Results:

  • Both MPTGA and the t-test identified H3N2 as the most distinct virus, showing the most virus-specific gene effects.
  • MPTGA demonstrated significantly higher detection power compared to the t-test.
  • Genes identified by MPTGA exhibited greater biological relevance.

Conclusions:

  • The transcriptome response to viral infection is a dynamic process.
  • The MPTGA method, by incorporating temporal gene expression data, enhances the detection of biologically relevant, virus-specific effects.
  • MPTGA offers a more powerful approach than conventional t-tests for analyzing dynamic host responses to viral infections.