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Bioactivation is a metabolic process that transforms less reactive substances into highly reactive metabolites, initiating tissue toxicity. This transformation can lead to various toxic effects, including carcinogenesis and teratogenesis. Reactive metabolites are classified into two main types: electrophiles and free radicals.Electrophiles are electron-deficient species and are produced primarily by the enzyme cytochrome P-450 during the metabolism of compounds containing carbon, nitrogen, or...
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Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

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Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set.

Natalia Polouliakh1,2,3, Takeshi Hase4,5,6, Samik Ghosh4

  • 1Sony Computer Science Laboratories Inc., Tokyo, Japan. nata@csl.sony.co.jp.

Methods in Molecular Biology (Clifton, N.J.)
|April 19, 2022
PubMed
Summary

Machine learning identified gene networks responding to the toxic chemical Pentachlorophenol. Two subnetworks, one involving Interferon and another Nuclear Factor 2 (NRF2), were revealed, highlighting innate immune system responses.

Keywords:
Gene expression analysisPhylogenetic footprintingReverse engineeringUnsupervised clustering

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Area of Science:

  • Computational biology
  • Toxicogenomics
  • Systems biology

Background:

  • Advancements in computational technologies enable sophisticated data analysis.
  • Understanding cellular responses to toxic chemicals is crucial for risk assessment and mitigation.

Purpose of the Study:

  • To demonstrate a machine learning workflow for analyzing gene expression data.
  • To identify gene regulatory networks involved in response to Pentachlorophenol exposure.

Main Methods:

  • Spectral clustering for data analysis.
  • Reverse engineering for network inference.
  • Analysis of gene expression data following Pentachlorophenol exposure.

Main Results:

  • Identified two distinct gene subnetworks: one orchestrated by Interferon and another by Nuclear receptor factor 2 (NRF2).
  • Discovered a gene network module associated with interferon signaling and innate immune responses.
  • Highlighted regulatory interactions with downstream genes, including TRIM family proteins.

Conclusions:

  • Machine learning provides powerful tools for dissecting complex biological responses to toxicants.
  • Interferon and NRF2 pathways play significant roles in the cellular defense against Pentachlorophenol.
  • The identified network provides insights into innate immunity regulation.