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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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What is Gene Expression?01:36

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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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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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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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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Early postmortem interval (EPMI) estimation using differentially expressed gene transcripts.

Hui Wang1, Jianlong Ma2, Hongmei Xu1

  • 1Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, 131 Dongan Road, Shanghai 200032, PR China.

Legal Medicine (Tokyo, Japan)
|May 21, 2019
PubMed
Summary
This summary is machine-generated.

Researchers developed a mathematical model using gene expression to estimate the early postmortem interval (PMI). Specific genes like Ninj2 and Grifin showed high accuracy in predicting time since death, aiding forensic science.

Keywords:
Early postmortem intervalForensic pathologyGene transcriptsR softwareThree-dimensional models

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Area of Science:

  • Forensic Science
  • Molecular Biology
  • Bioinformatics

Background:

  • Accurate postmortem interval (PMI) estimation is crucial in forensic investigations.
  • Traditional methods for PMI estimation have limitations, especially in early stages.
  • Gene expression changes after death offer potential biomarkers for PMI determination.

Purpose of the Study:

  • To develop a mathematical model for early postmortem interval estimation using differentially expressed genes.
  • To identify and validate stable gene markers for accurate PMI prediction.
  • To create a visual statistical model for practical application in forensic analysis.

Main Methods:

  • Differential gene expression analysis using microarray profiling in rat brain tissue postmortem.
  • Validation of gene transcript levels via reverse transcription quantitative polymerase chain reaction (RT-qPCR) at various temperatures.
  • Construction of mathematical models using R software and visualization with MATLAB.

Main Results:

  • Six genes with high coefficients of determination were selected for model construction.
  • The gene 5srRNA demonstrated the highest stability across tested temperatures.
  • Genes Ninj2, Grifin, Arpp19, and Hopx exhibited high accuracy (>80% R²) and low error (<3h) in validation, indicating their potential as early PMI markers.

Conclusions:

  • A novel mathematical model based on gene expression can accurately estimate early postmortem intervals.
  • Specific genes (Ninj2, Grifin, Arpp19, Hopx) are promising biomarkers for early PMI estimation.
  • The developed models and visualization tools can aid forensic investigations in determining time since death.