Genome-wide detection of transcription errors in bacteria
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Errors in DNA that occur during replication serve as the basis for adaptation and heritable genetic variation in all organisms. However, non-heritable genetic variation will arise through errors that occur during transcription. Although it has been hypothesized that errors in transcripts might aid in survival of antibiotic stress and help evade immune responses, they can be deleterious in that they cause RNA polymerase (RNAP) to pause, resulting in collisions between the RNAP and replication proteins. Additionally, too many errors within the proteome can result in aggregation of these faulty proteins, inducing the general stress response. To avoid such complications, bacteria have evolved numerous mechanisms to improve the fidelity of transcription. These mechanisms include recognition of mis-paired bases within the RNAP, recognition of slippage along the template, excision of errors from the transcript, and prevention of errors from occurring. Despite decades of research, accurate measurements of the transcription error rate have remained elusive. Although recent sequencing-based measurements can provide ways to assay errors genome-wide, RNAseq error rates are too high to gauge the various types of substitutions. Additionally, measurement of transcription slippage remains limited to the analysis of long homopolymeric repeats in reporter genes. This dissertation reports the use of a sequencing technique that allows us to detect transcription errors and remove the errors that arise during sequencing. This method was successful in determining the genome-wide transcription substitution, insertion, and deletion rates. We found that transcription error rates remain constant across a wide range of growth states and across phylogenetically diverse bacteria. These data also suggest that transcription slippage occurs in sequences that are more complex than homopolymeric runs. Finally, we find that only one of the three previously identified transcription fidelity factors appears to influence transcription fidelity.