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Updated: Sep 28, 2025

Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
Published on: April 26, 2019
Pervasive translation in Mycobacterium tuberculosis
Carol Smith1, Jill G Canestrari1, Archer J Wang1
1Wadsworth Center, Division of Genetics, New York State Department of Health, Albany, United States.
Researchers discovered many new, short protein-coding regions in Mycobacterium tuberculosis using ribosome profiling. Some of these newly found ORFs (open reading frames) may evolve into new functional proteins, enhancing our understanding of bacterial gene expression.
Area of Science:
- Microbiology
- Genomics
- Molecular Biology
Background:
- Automated algorithms often miss bacterial ORFs (open reading frames) lacking standard features like sufficient length or Shine-Dalgarno sequences.
- Understanding the full extent of translation in Mycobacterium tuberculosis is crucial for comprehending its biology.
Purpose of the Study:
- To identify actively translated ORFs in Mycobacterium tuberculosis using ribosome profiling.
- To characterize the features and potential functions of newly discovered ORFs.
Main Methods:
- Ribosome profiling was employed to capture actively translated regions of the Mycobacterium tuberculosis transcriptome.
- Bioinformatic analyses, including codon usage, were performed on identified ORFs.
Main Results:
- A significant number of previously undescribed ORFs were identified, indicating pervasive translation in Mycobacterium tuberculosis.
- Many newly identified ORFs were short (≤50 amino acids), with some showing signs of purifying selection.
- Ninety new ORFs (median length 52 codons) exhibited hallmarks of purifying selection, suggesting functional potential.
Conclusions:
- The Mycobacterium tuberculosis transcriptome is pervasively translated, including numerous short ORFs.
- These newly identified ORFs represent a potential reservoir for the evolution of novel functional proteins.
- Ribosome profiling is effective for discovering functionally relevant ORFs missed by traditional methods.
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