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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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Transposons, or "jumping genes," are small mobile genetic elements (MGEs) that range from 700 to 40,000 base pairs in length. They are found in all organisms and can move within the same chromosome or transfer to different chromosomes. In some cases, transposons can also jump between different host DNA molecules, such as plasmids or viruses, contributing to genetic variability.Barbara McClintock first discovered these mobile genetic elements in the 1940s while studying maize genetics, and she...
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Decoding post-transcriptional gene expression controls in trypanosomatids using machine learning.

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Summary
This summary is machine-generated.

Gene expression in trypanosomatids is controlled by untranslated regions (UTRs) and codon usage bias, primarily impacting translation efficiency and mRNA levels across species.

Keywords:
Codon BiasLeishmaniaMachine LearningTranslation EfficiencyTrypanosomaUTRs

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Area of Science:

  • Molecular Biology
  • Genetics
  • Parasitology

Background:

  • Untranslated regions (UTRs) in Trypanosoma brucei mRNA possess cis-regulatory roles.
  • A-rich tracts in UTRs correlate with increased translation efficiency (TE).
  • Key questions address UTRs, codon usage bias, and their impact on TE and mRNA levels in T. brucei, T. cruzi, and Leishmania.

Purpose of the Study:

  • To determine the relative contributions of UTRs and codon usage bias to translation efficiency (TE) in T. brucei.
  • To assess the impact of UTRs and codon usage bias on mRNA steady-state levels in T. brucei.
  • To investigate the role of these sequences in TE and mRNA levels in related trypanosomatids, T. cruzi and Leishmania.

Main Methods:

  • Machine learning approaches were employed.
  • Analysis of existing transcriptome, translation efficiency, and proteomics data.
  • Comparative analysis across three trypanosomatid species.

Main Results:

  • Both UTRs and codon usage bias influence gene expression in all three trypanosomatids, with species-specific differences.
  • In T. brucei, TE correlates with longer A-rich and C-poor UTRs.
  • Codon usage bias significantly impacts mRNA abundance across all species, potentially via translation elongation rates.

Conclusions:

  • Gene expression control in trypanosomatids is primarily post-transcriptional, at the translation level.
  • UTRs influence translation initiation rates.
  • Favored codons enhance translation elongation, reducing mRNA turnover and increasing stability.